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The aim of this study was to investigate the reference population size required to obtain substantial prediction accuracy within‐ and across‐lines and the effect of using a multi‐line reference population for genomic predictions of maternal traits in pigs. The data consisted of two nucleus pig populations, one pure‐bred Landrace (L) and one Synthetic (S) Yorkshire/Large White line. All animals were genotyped with up to 30 K animals in each line, and all had records on maternal traits. Prediction accuracy was tested with three different marker data sets: High‐density SNP (HD), whole genome sequence (WGS), and markers derived from WGS based on pig combined annotation dependent depletion‐score (pCADD). Also, two different genomic prediction methods (GBLUP and Bayes GC) were compared for four maternal traits; total number piglets born (TNB), total number of stillborn piglets (STB), Shoulder Lesion Score and Body Condition Score. The main results from this study showed that a reference population of 3 K–6 K animals for within‐line prediction generally was sufficient to achieve high prediction accuracy. However, when the number of animals in the reference population was increased to 30 K, the prediction accuracy significantly increased for the traits TNB and STB. For multi‐line prediction accuracy, the accuracy was most dependent on the number of within‐line animals in the reference data. The S‐line provided a generally higher prediction accuracy compared to the L‐line. Using pCADD scores to reduce the number of markers from WGS data in combination with the GBLUP method generally reduced prediction accuracies relative to GBLUP using HD genotypes. The BayesGC method benefited from a large reference population and was less dependent on the different genotype marker datasets to achieve a high prediction accuracy. [ABSTRACT FROM AUTHOR]

The dengue virus (DENV) is a mosquito-borne flavivirus transmitted through the infected mosquito, primarily of the Aedes genus. Over recent decades, the significant emergence of DENV infections has posed growing challenges for control efforts. Extensive research indicates that the dengue NS2B–NS3 protease, a non-structural viral protein, is the most promising target in dengue drug research. Therefore, our study synthesized two series of novel allosteric inhibitors of the NS2B–NS3 protease to explore previous allosteric inhibitors of the protease. Meanwhile, enzyme inhibitory activities of all compounds are measured and IC50 values of the optimized compounds are obtained by utilizing a fluorescence-based assay. Notably, compound A24 demonstrates the highest enzyme inhibitory activity with an IC50 value of 16 μM. Further enzyme kinetic experiments confirm that the optimized compound A24 functions as a non-competitive inhibitor of the NS2B–NS3 protease. Through molecular docking studies, compound A24 showed a binding energy of −5.6 kcal mol−1, comparable to the lead compounds, which preliminarily explains its similar inhibitory activity. In addition, compound A24 forms a stable complex with the protein primarily through hydrogen bonds and π–π interaction, with subsequent dynamics simulations confirming the stable binding mode. In conclusion, we designed and synthesized 59 novel scaffold compounds and identified compound A24 with the highest activity through pharmacological experiments and computer-aided drug design, which targets the allosteric sites and contributes significantly to the prevention of the dengue virus. [ABSTRACT FROM AUTHOR]

Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies. [ABSTRACT FROM AUTHOR]

Since the advent of computational analysis and visualization of chemical compounds, Computer-Aided Drug Design has made significant contributions to drug discovery. Recently, de novo drug design and molecular optimization have garnered considerable attention. Traditional optimization methods often struggle with the discrete nature of molecular space, but evolutionary computations have demonstrated their versatility across various optimization problems, regardless of the nature of the objective functions. This paper introduces a novel evolutionary algorithm, the Swarm Intelligence-Based Method for Single-Objective Molecular Optimization. Several experiments were conducted to showcase the efficiency of the proposed method, which identifies near-optimal solutions in a remarkably short time. The results were then compared with those of other state-of-the-art methods in the field. [ABSTRACT FROM AUTHOR]

Gliomas, particularly glioblastoma (GBM), are highly aggressive brain tumors with poor prognosis and high recurrence rates. This underscores the urgent need for novel therapeutic approaches. One promising target is Focal adhesion kinase (FAK), a key regulator of tumor progression currently in clinical trials for glioma treatment. Drug development, however, is both challenging and costly, necessitating efficient strategies. Computer-Aided Drug Design (CADD), especially when combined with machine learning (ML), streamlines the processes of virtual screening and optimization, significantly enhancing the efficiency and accuracy of drug discovery. Our study integrates ML, docking analysis, ADMET (absorption, distribution, metabolism, elimination, and toxicity) studies to identify novel FAK inhibitors specific to GBM. Predictive models showed strong performance, with an R2 of 0.892, MAE of 0.331, and RMSE of 0.467 using protein-level IC50 data in combined CDK, CDK extended fingerprints, and substructure fingerprint counts derived from 1280 FAK inhibitors. Another model, based on IC50 data from 2608 compounds tested on U87-MG cells, achieved an R2 of 0.789, MAE of 0.395, and RMSE of 0.536. Using these models, we efficiently identified 275 potentially active compounds out of 5107 candidates. Subsequent ADMET analysis narrowed this down to 16 potential FAK inhibitors that meet the established drug-likeness criteria. Moreover, molecular dynamics (MD) simulations validated the stable binding interactions between the selected compounds and the FAK protein. This study highlights the effectiveness of combining ML, docking analysis, and ADMET studies to rapidly identify potential FAK inhibitors from large databases, providing valuable insights for the systematic design of FAK inhibitors. [ABSTRACT FROM AUTHOR]

Introduction: Hepatic carcinoma (HCC) is one of the most lethal malignant tumors in the world, and new treatment regimens for this disease are urgently needed. Studies have shown that thrombin stimulates tumor progression by forming fibrin and activating platelets. Dabigatran etexilate, a thrombin inhibitor, can inhibit the activity of thrombin and prevent the proliferation and metastasis of HCC in cells and nude mice. Methods: The present study was designed to find thrombin inhibitors with novel skeletons, and further confirm the correlation between thrombin inhibition and HCC prevention to identify potential anti-HCC drug leads. Results and Discussion: The potential thrombin inhibitors were firstly screened in the Topscience Database, and 20 potential active molecules were found by molecular docking. The effect of these molecules on thrombin inhibition, coagulation and tumor proliferation were evaluated, and the definite activity of ZXX-4 was identified. Further in vivo assays in nude mice showed that ZXX-4 inhibited tumor proliferation in nude mice, reduced tumor metastasis, and enhanced the clinical efficacy of first-line drug sorafenib for the treatment of HCC. ZXX-4 can be further explored as an anti-tumor lead compound with a novel skeleton, and inhibition of thrombin can serve as a potential treatment strategy for HCC. [ABSTRACT FROM AUTHOR]

Perry disease (PeD) is a rare, neurodegenerative, genetic disorder inherited in an autosomal dominant manner. The disease manifests as parkinsonism, with psychiatric symptoms on top, such as depression or sleep disorders, accompanied by unexpected weight loss, central hypoventilation, and aggregation of DNA-binding protein (TDP-43) in the brain. Due to the genetic cause, no causal treatment for PeD is currently available. The only way to improve the quality of life of patients is through symptomatic therapy. This work aims to review the latest data on potential PeD treatment, specifically from the medicinal chemistry and computer-aided drug design (CADD) points of view. We select proteins that might represent therapeutic targets for symptomatic treatment of the disease: monoamine oxidase B (MAO-B), serotonin transporter (SERT), dopamine D2 (D2R), and serotonin 5-HT1A (5-HT1AR) receptors. We report on compounds that may be potential hits to develop symptomatic therapies for PeD and related neurodegenerative diseases and relieve its symptoms. We use Phase pharmacophore modeling software (version 2023.08) implemented in Schrödinger Maestro as a ligand selection tool. For each of the chosen targets, based on the resolved protein–ligand structures deposited in the Protein Data Bank (PDB) database, pharmacophore models are proposed. We review novel, active compounds that might serve as either hits for further optimization or candidates for further phases of studies, leading to potential use in the treatment of PeD. [ABSTRACT FROM AUTHOR]

Zoo populations of threatened species are a valuable resource for the restoration of wild populations. However, their small effective population size poses a risk to long‐term viability, especially in species with high genetic load. Recent bioinformatic developments can identify harmful genetic variants in genome data. Here, we advance this approach, analysing the genetic load in the threatened pink pigeon (Nesoenas mayeri). We lifted the mutation‐impact scores that had been calculated for the chicken (Gallus gallus) to estimate the genetic load in six pink pigeons. Additionally, we perform in silico crossings to predict the genetic load and realized load of potential offspring. We thus identify the optimal mate pairs that are theoretically expected to produce offspring with the least inbreeding depression. We use computer simulations to show how genomics‐informed conservation can reduce the genetic load whilst reducing the loss of genome‐wide diversity. Genomics‐informed management is likely to become instrumental in maintaining the long‐term viability of zoo populations. see also the Perspective by Evelyn L. Jensen, Rachel Gray & Joshua M. Miller [ABSTRACT FROM AUTHOR]

The article discusses the creation of the World Wide Approved Drug (WWAD) database, which aims to provide comprehensive information on medicines approved for therapeutic use worldwide. The database collects data from National Medicine Registers (NMRs) and official documentation from medicines regulatory authorities (MRAs) of different countries. The article emphasizes the need for a unified resource that aggregates information on worldwide approved medicines and highlights the challenges in accessing and processing this data. The World Wide Approved Drugs (WWAD) database is a comprehensive resource that provides information on low molecular weight pharmaceutical substances from 71 countries and regions. It includes data on drug names, chemical structures, therapeutic indications, molecular targets, and biological activities. The database can be used for various purposes, including searching for information on approved drugs, drug repositioning, data enrichment, and assessing the novelty of investigated compounds. The WWAD database is freely available for academic use through the Way2Drug platform. This document contains a list of references and citations from various scientific articles and databases related to topics such as computational chemistry, drug discovery, drug repurposing, and regulatory approval of drugs. The references cover a range of subjects including the use of artificial intelligence and machine learning in drug discovery, the development of databases for drug repurposing, and the analysis of drug approvals by regulatory agencies. The document provides a comprehensive list of sources that library patrons can use to further their research on these topics. [Extracted from the article]

The World Health Organization has identified antibiotic resistance as one of the three greatest threats to human health. The need for antibiotics is a pressing matter that requires immediate attention. Here, computer-aided drug design is used to develop a structurally unique antibiotic family targeting holo-acyl carrier protein synthase (AcpS). AcpS is a highly conserved enzyme essential for bacterial survival that catalyzes the first step in lipid synthesis. To the best of our knowledge, there are no current antibiotics targeting AcpS making this drug development program of high interest. We synthesize a library of > 700 novel compounds targeting AcpS, from which 33 inhibit bacterial growth in vitro at ≤ 2 μg/mL. We demonstrate that compounds from this class have stand-alone activity against a broad spectrum of Gram-positive organisms and synergize with colistin to enable coverage of Gram-negative species. We demonstrate efficacy against clinically relevant multi-drug resistant strains in vitro and in animal models of infection in vivo including a difficult-to-treat ischemic infection exemplified by diabetic foot ulcer infections in humans. This antibiotic family could form the basis for several multi-drug-resistant antimicrobial programs. Holo-acyl carrier protein synthase (AcpS) is an enzyme that catalyses the first step in lipid synthesis and is essential for bacterial survival, but no current antibiotics targeting AcpS are known. Here, the authors use computer-aided drug design to develop a structurally unique antibiotic family targeting AcpS. [ABSTRACT FROM AUTHOR]

In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect–the hydration free energy (HFE) of small molecules–is often insufficiently analyzed. While most benchmarking studies report excellent accuracies of calculated hydration free energies–usually within 2 kcal/mol of experimental values–we find that deeper analysis reveals significant shortcomings. Herein, we report a dependence of HFE prediction errors on ligand molecular weight–the higher the weight, the bigger the prediction error and the higher the probability the calculated result is erroneous by a large amount. We show that in the drug-like molecular weight region, HFE predictions can easily be off by 5 kcal/mol or more. This is likely to be highly problematic in a drug discovery and development setting. We make our HFE results and molecular descriptors freely and fully available in order to encourage deeper analysis of future molecular dynamics results and facilitate development of the next generation of force fields. [ABSTRACT FROM AUTHOR]

Background: The increasing prevalence of diabetes and the side effects associated with current medications necessitate the development of novel candidate drugs targeting alpha-glucosidase as a potential treatment option. Methods: This study employed computer-aided drug design techniques to identify potential alpha-glucosidase inhibitors from the PubChem database. Molecular docking was used to evaluate 81,197 compounds, narrowing the set for further analysis and providing insights into ligand-target interactions. An ADMET study assessed the pharmacokinetic properties of these compounds, including absorption, distribution, metabolism, excretion, and toxicity. Molecular dynamics simulations validated the docking results. Results: 9 compounds were identified as potential candidate drugs based on their ability to form stable complexes with alpha-glucosidase and their favorable pharmacokinetic profiles, three of these compounds were subjected to the molecular dynamics, which showed stability throughout the entire 100 ns simulation. Conclusion: These findings suggest promising new alpha-glucosidase inhibitors for diabetes treatment. Further validation through in vitro and in vivo studies is recommended to confirm their efficacy and safety. [ABSTRACT FROM AUTHOR]

This article explores the integration of artificial intelligence (AI) in clinical pharmacology and its potential benefits, challenges, and ethical considerations. AI, including machine learning and deep learning, can analyze large datasets to provide insights into drug efficacy and patient outcomes. It has the potential to accelerate drug discovery, optimize clinical trials, and improve personalized medicine. However, challenges such as patient safety, regulatory landscapes, algorithmic bias, data privacy, and ethical considerations must be carefully managed. The article emphasizes the importance of robust clinical evaluations, intuitive metrics, multidisciplinary efforts, and stringent ethical standards to ensure responsible and beneficial use of AI in clinical pharmacology. It also highlights the need for transparency, accountability, secure access, education, and legal frameworks to prevent misuse and enforce ethical standards. The article acknowledges the potential of AI in drug discovery and personalized medicine but cautions against replacing rigorous quantitative methods in clinical pharmacology. It concludes by emphasizing the need for AI education in healthcare training programs and the evaluation of AI's impact on patient outcomes. [Extracted from the article]

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 μ M; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents. [ABSTRACT FROM AUTHOR]

Essential oils (EOs) and plant extracts, rich in beneficial chemical compounds, have diverse applications in medicine, food, cosmetics, and agriculture. This study investigates the antibacterial activity of nine essential oil constituents (EOCs) against Escherichia coli, focusing on the effects of treatment pH and biosynthetic requirements. The impact of EOCs on bacterial inactivation in E. coli strains was examined using both nonselective and selective culture media. Computer-assisted drug design (CADD) methods were employed to identify critical binding sites and predict the main binding modes of ligands to proteins. The EOCs, including citral, α-terpinyl acetate, α-terpineol, and linalool, demonstrated significant bacterial inactivation, particularly under acidic conditions. This study revealed that EOCs have an effect on the presence of sublethal damage to both the cytoplasmic membrane and the outer membrane in Gram-negative bacteria. Adding penicillin G to the repair medium prevents the recovery of sublethal injuries in E. coli treated with α-terpinyl acetate, α-terpineol, linalool, and citral, indicating that peptidoglycan synthesis is essential for recovering from these injuries. However, penicillin G did not hinder the recovery process of most sublethally injured cells treated with the other assessed EOCs. Molecular docking studies revealed the favorable binding interactions of α-terpinyl acetate, α-terpineol, linalool, and citral with the β-lactamase enzyme Toho-1, indicating their potential as effective antibacterial agents. The findings suggest that EOCs could serve as viable alternatives to synthetic preservatives, offering new strategies for combating antibiotic-resistant bacteria. [ABSTRACT FROM AUTHOR]

This research implements a computer-aided formulation development algorithm based on a novel SeDeM-ODT expert system in establishing the design space for paediatric pyridoxine hydrochloride orodispersible tablets (ODTs) using Prosolv® ODTG2, Prosolv® EasyTab SP, and Ludiflash® systems. For each formulation ingredient, expert system-defined preformulation parameter values were experimentally determined according to standardized methods and then normalized to the theoretical radius range [0,10]. Expert diagrams were constructed and the quantitative performance of each ingredient was evaluated using parametric profile index (IPP), flowability (ff'), and compressibility (ffc) functions. The net direct compression capability was quantitatively expressed as the product of expert system reliability and IPP. Direct compression was conducted in an eccentric tablet press and properties were evaluated using weight, dimension, disintegration test, contact angle, tensile strength, x-ray diffraction, and Fourier-transform infrared spectroscopy. The ODTs dissolution profiles were fitted and compared using zero-order, first-order, Hixson-Crowell, and Hopfenberg models. Results of the expert diagram of pyridoxine hydrochloride indicated suboptimal normalized radii values in 8 out of 12 parameters, implying a compromised mechanical zone (ff'=3.61, ffc=2.11). By setting a target ffc for the optimized formulation mix at 5.0, the predicted proportions of the fillers to remedy the direct compression deficits of the drug were computed as 89.00%, 83.23%, and 76.62% for Prosolv® ODTG2 (ffc=5.36), Prosolv® EasyTab SP (ffc=5.58), and Ludiflash® (ffc=5.88), respectively. The produced ODTs were of acceptable target quality, hence the SeDeM-ODT system was considered a reliable formulation tool for establishing the design space of this particular drug-filler systems. [ABSTRACT FROM AUTHOR]

Citrobacter koseri is a gram-negative rod that causes infections in people who have significant comorbidities and are immunocompromised. Antibiotic-resistant strains are becoming more common, which complicates infection treatment and highlights the need for innovative, effective drugs to fight these resistant strains. The enzyme complex ATP synthase participates in the adenosine triphosphate (ATP) synthesis, the fundamental energy currency of cells. This study used Computer-Aided Drug Design approaches to identify potential inhibitors of C. koseri ATP synthase. SWISS-MODEL was used to predict the 3D structure of C. koseri ATP synthase. A ligand-based pharmacophore model was developed using chemical features of ampicillin. Following ligand-based virtual screening across nine databases, the 2043 screened hits were docked to the ATP synthase active site using the standard precision mode of the glide tool. Based on their binding affinities, the top ten compounds were selected for additional investigation. The binding affinities of the chosen compounds ranged from -10.021 to -8.452 kcal/mol. The top four compounds (PubChem-25230613, PubChem-74936833, CHEMBL263035, PubChem-44208924) with the best ADMET characteristics and binding modes were chosen. Thus, the feasible binding mechanisms of the selected compounds were subjected to stability analysis using the MD Simulation study, which revealed the compounds' stability as potent inhibitors within the protein binding pocket. This computational approach provides important insights into the rational design of novel therapeutics and emphasizes the importance of targeting essential metabolic pathways when combating antibiotic-resistant pathogens. Future experimental validation and optimization of the identified inhibitors is required to determine their efficacy and safety profiles for clinical use. [ABSTRACT FROM AUTHOR]

In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv. Scientific contribution We propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms. [ABSTRACT FROM AUTHOR]

Context: Although quantum mechanical calculations have proven effective in accurately predicting UV absorption and assessing the antioxidant potential of compounds, the utilization of computer-aided drug design (CADD) to support sustainable synthesis research of new sunscreen active ingredients remains an area with limited exploration. Furthermore, there are ongoing concerns about the safety and effectiveness of existing sunscreens. Therefore, it remains crucial to investigate photoprotection mechanisms and develop enhanced strategies for mitigating the harmful effects of UVR exposure, improving both the safety and efficacy of sunscreen products. A previous study conducted synthesis research on eight novel hybrid compounds (I–VIII) for use in sunscreen products by molecular hybridization of trans-resveratrol (RESV), avobenzone (AVO), and octinoxate (OMC). Herein, time-dependent density functional theory (TD-DFT) calculations performed in the gas phase on the isolated hybrid compounds (I–VIII) proved to reproduce the experimental UV absorption. Resveratrol-avobenzone structure-based hybrids (I–IV) present absorption maxima in the UVB range with slight differences between them, while resveratrol-OMC structure-based hybrids (V–VIII) showed main absorption in the UVA range. Among RESV-OMC hybrids, compounds V and VI exhibited higher UV absorption intensity, and compound VIII stood out for its broad-spectrum coverage in our simulations. Furthermore, both in silico and in vitro analyses revealed that compounds VII and VIII exhibited the highest antioxidant activity, with compound I emerging as the most reactive antioxidant within RESV-AVO hybrids. The study suggests a preference for the hydrogen atom transfer (HAT) mechanism over single-electron transfer followed by proton transfer (SET-PT) in the gas phase. With a strong focus on sustainability, this approach reduces costs and minimizes effluent production in synthesis research, promoting the eco-friendly development of new sunscreen active ingredients. Methods: The SPARTAN'20 program was utilized for the geometry optimization and energy calculations of all compounds. Conformer distribution analysis was performed using the Merck molecular force field 94 (MMFF94), and geometry optimization was carried out using the parametric method 6 (PM6) followed by density functional theory (DFT/B3LYP/6-31G(d)). The antioxidant behavior of the hybrid compounds (I–VIII) was determined using the highest occupied molecular orbital (εHOMO) and the lowest unoccupied molecular orbital (εLUMO) energies, as well as the bond dissociation enthalpy (BDE), ionization potential (IP), and proton dissociation enthalpy (PDE) values, all calculated at the same level of structural optimization. TD-DFT study is carried out to calculate the excitation energy using the B3LYP functional with the 6-31G(d) basis set. The calculated transitions were convoluted with a Gaussian profile using the Gabedit program. [ABSTRACT FROM AUTHOR]

Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance. [ABSTRACT FROM AUTHOR]

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative –omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics. [ABSTRACT FROM AUTHOR]

Motivation Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent. Results Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding. Availability and implementation Code and example datasets are available at https://github.com/pgwall/qms. [ABSTRACT FROM AUTHOR]

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort—however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics. [ABSTRACT FROM AUTHOR]

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators. [ABSTRACT FROM AUTHOR]

The impact of the LOXL2 enzyme on cell invasion and metastasis is significantly notable in breast cancer (BC), making it a promising therapeutic target in cancer drug discovery. Considering the diverse functions of lysyl oxidase both extracellularly and intracellularly, inhibiting LOX activity emerges as a strategic approach to impede cancer progression and invasion. This study is uniquely designed to identify potential small molecules targeting LOXL2 in BC. Initially, we designed small molecuels and conducted docking studies using ChemDraw and AutoDock Vina, respectively. Assessment of ADME, drug-likeness and toxicological properties was carried out using online tools such as SwissADME, admetSAR, pkCSM and ProTox-II. The best docked candidates underwent further evaluation through 100 ns molecular dynamics simulations. Compound H.2.9 scored of −8.4 kcal/mol in docking, surpassing the control's (US10717733) −5.0 kcal/mol, indicating its potential as an LOXL2 inhibitor. ADME predictions indicated that all screened compounds adhere to Lipinski's rule of five. Additionally, toxicological predictions confirmed that these compounds are non-cytotoxic, non-hepatotoxic and non-carcinogenic. The RMSD, RMSF and MM-GBSA analyses affirmed the stability of the H.2.9 compound. The development of LOXL2 inhibitors instils optimism for effective therapies against both primary and metastatic cancers in the foreseeable future, contributing to advancements in cancer research. Abbreviations: 2D, Two-dimensional; 3D, Three-dimensional; ADMET, Absorption, distribution, metabolism, excretion and toxicity; CADD, Computer-aided drug design; MD, Molecular dynamics; MMFF, Merck molecular force field; MOL, Molecular structure file; PDB, Protein data bank; PK, Pharmacokinetics; OPLS, Optimised potentials for liquid simulations; RMSD, Root mean square deviation; RMSF, Root mean square fluctuation; SASA, Solvent-accessible surface area; SDF, Structure data file; SMILES, Simplified molecular input line entry system; SPC, Simple-point charge [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a chronic metabolic disorder posing a significant health risk. Effective treatments are continually sought. Coumarin derivatives with oxime ester groups have shown potential as antidiabetic agents by inhibiting the α-glucosidase enzyme, a key player in glycoprotein metabolism and postprandial hyperglycemia control. This makes lysosomal α-glucosidase a promising therapeutic target. A study used 28 coumarin derivatives with known α-glucosidase inhibitory IC50 values for computer-assisted drug design (CADD) through quantitative structure-activity relationship (QSAR) analysis, yielding a statistically robust equation for guiding new compound development. Subsequently, eleven new coumarin derivatives with oxime ester groups were synthesized, showing enhanced α-glucosidase inhibitory activity compared to the initial set. Molecular docking assays indicated that compounds 32, 37, 38, and 39 had lower free energy values than the native ligand, suggesting higher stability in target protein interactions. Notably, compound 38 had the lowest free energy (-8.351) and demonstrated lower root mean square deviation (RMSD) and root mean square fluctuation (RMSF) than the original ligand, indicating greater stability. The QSAR equation derived is: Log IC50 = 2.886 - 0.054 (LUMO) + 0.073 (μ) – 0.148 (α) – 0.046 (RD) + 0.046 (BM) + 0.001 (Vvdw) – 0.421 (qC2) + 1.138 (qC8) – 0.092 (qC9) + 2.61 (qC10) + 1.354 (qN1) (Eq 1) n=28; R=0.918; R²=0.843; SD=0.196; F calc/F tab=3.169; Sig =<0.01; PRESS = 1.376. Compound 38’s SMILES notation is: C\C(=N/OC(=O)\C=C/C1=CC=C(Br)C=C1)C1=CC2=CC(O)=C(CC(O)=O)C=C2OC1=O). [ABSTRACT FROM AUTHOR]

Diabetes mellitus is one of the chronic metabolic disorders that affects more than 16 million Filipinos. Proper education, medical intervention, and a good lifestyle can help individuals control and manage this disease. Spondias pinnata is one of the underutilized crops in the Philippines that is well-known for its satisfactory flavor and medicinal properties, including its antidiabetic activity. The quest for a natural and effective drug to manage diseases is a continuous work in progress. Drug discovery and design is a tedious and expensive process. Computer-aided drug design guides the design and makes the process more efficient and less costly. Molecular docking was used to determine the potential antidiabetic compounds from the 48 reported compounds found in S. pinnata fruit. Seven compounds namely squalene (−9.1 kcal/mol), rutin (−9 kcal/mol), catechin (−8.7 kcal/mol), quercetin (−8.5 kcal/mol), tocopherol (−8.4 kcal/mol), myricetin (−8.4 kcal/mol), and ellagic acid (−8.3 kcal/mol) showed binding affinities comparable to those of pioglitazone, a standard drug, with peroxisome proliferator-activated receptor gamma (PPARγ). Tocopherol and catechin showed good ADMET properties. Among the two compounds, catechin passed the four filters for drug-likeness. Thus, catechin could be a potential compound for the development of antidiabetic drugs. [ABSTRACT FROM AUTHOR]

Polyphenols, the important secondary metabolites, consist of multiple phytochemicals and show numerous physiological effects. Flavones play a significant role in various chronic disorders such as diabetes.. In this study, all the flavones were encountered, and it was further filtered based on their drug-likeness properties and pharmacokinetic parameters. Existing literature confirms that flavone-based compounds are suitable as the drug of choice in sarcopenic obesity. A molecular docking study was performed toward the myostatin inhibition profile of the flavones using PDB:3HH2 as a target site. This computer-aided drug design helps select lead molecules in novel drug discovery. [ABSTRACT FROM AUTHOR]

Visceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite's N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the potential inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyloligosaccharide Glucosidase Protein (MOGS) from Leishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite's changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation through in vitro and in vivo experiments. [ABSTRACT FROM AUTHOR]

Background: Allergic disorders, prevalent global health concerns, afflict a substantial portion of the world's population. These maladies result from an exaggerated immune system response to ordinarily innocuous substances, such as pollen, dust mites, and specific dietary components. Clinical manifestations of this heightened immune response include itching, swelling, and respiratory impairment, often accompanied by releasing mediators like histamine. The pathophysiological mechanisms of allergy disorders are intricate, arising from a complex interplay between genetic and environmental factors. While clinical presentations may vary, all allergy conditions share a common foundation in the dysregulated immune response to allergens. Result: The current aim of this study was to identify innovative anti-allergic agents capable of inhibiting histamine and effectively mitigating allergic reactions by utilizing the computer-aided drug design approach by discovery studio (DS) 2022 v 23.1.1 package. The overarching aim was identifying potential drug candidates targeting the active site within the histamine H1 receptor complex; therefore, a collection of 4000 small druggable compounds was curated from ZINC, PubChem, and DRUG BANK databases sources. Four compounds appeared as promising candidates after assessing docking scores and binding energies. Notably, Compound ID 34154, recognized as tymazoline, showed the highest affinity for the H1 receptor of 3RZE, suggesting it may be the most promising choice for more research. Further chemoinformatic and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analyses were conducted to assess the drug-like qualities of this chosen molecule. In addition, bioisosteric substitution techniques were employed to enhance tymazoline's ADMET characteristics. Conclusion: Tymazoline shows strong binding affinity with 3RZE and verified all the drug-likeness criteria to inhibit the allergic disorders. Furthermore, molecular dynamics (MD) studies corroborated tymazoline's potential as an anti-allergic agent, demonstrating contact between the ligand and the receptor that is well defined and stable. [ABSTRACT FROM AUTHOR]

Despite significant progress made over the past two decades in the treatment of chronic myeloid leukemia (CML), there is still an unmet need for effective and safe agents to treat patients with resistance and intolerance to the drugs used in clinic. In this work, we designed 2-arylaminopyrimidine amides of isoxazole-3-carboxylic acid, assessed in silico their inhibitory potential against Bcr-Abl tyrosine kinase, and determined their antitumor activity in K562 (CML), HL-60 (acute promyelocytic leukemia), and HeLa (cervical cancer) cells. Based on the analysis of computational and experimental data, three compounds with the antitumor activity against K562 and HL-60 cells were identified. The lead compound efficiently suppressed the growth of these cells, as evidenced by the low IC50 values of 2.8 ± 0.8 μM (K562) and 3.5 ± 0.2 μM (HL-60). The obtained compounds represent promising basic structures for the design of novel, effective, and safe anticancer drugs able to inhibit the catalytic activity of Bcr-Abl kinase by blocking the ATP-binding site of the enzyme. [ABSTRACT FROM AUTHOR]

Parasitic diseases, predominantly prevalent in developing countries, are increasingly spreading to high-income nations due to shifting migration patterns. The World Health Organization (WHO) estimates approximately 300 million annual cases of giardiasis. The emergence of drug resistance and associated side effects necessitates urgent research to address this growing health concern. In this study, we evaluated over eleven thousand pharmacological compounds sourced from the FDA database to assess their impact on the TATA-binding protein (TBP) of the early diverging protist Giardia lamblia, which holds medical significance. We identified a selection of potential pharmacological compounds for combating this parasitic disease through in silico analysis, employing molecular modeling techniques such as homology modeling, molecular docking, and molecular dynamics simulations. Notably, our findings highlight compounds DB07352 and DB08399 as promising candidates for inhibiting the TBP of Giardia lamblia. Also, these compounds and DB15584 demonstrated high efficacy against trophozoites in vitro. In summary, this study identifies compounds with the potential to combat giardiasis, offering the prospect of specific therapies and providing a robust foundation for future research. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein–ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 μM, 6.7 μM, and 7.6 μM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 μM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 μM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection. [ABSTRACT FROM AUTHOR]

Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one's life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones. [ABSTRACT FROM AUTHOR]

IntroductionHepatic carcinoma (HCC) is one of the most lethal malignant tumors in the world, and new treatment regimens for this disease are urgently needed. Studies have shown that thrombin stimulates tumor progression by forming fibrin and activating platelets. Dabigatran etexilate, a thrombin inhibitor, can inhibit the activity of thrombin and prevent the proliferation and metastasis of HCC in cells and nude mice.MethodsThe present study was designed to find thrombin inhibitors with novel skeletons, and further confirm the correlation between thrombin inhibition and HCC prevention to identify potential anti-HCC drug leads.Results and DiscussionThe potential thrombin inhibitors were firstly screened in the Topscience Database, and 20 potential active molecules were found by molecular docking. The effect of these molecules on thrombin inhibition, coagulation and tumor proliferation were evaluated, and the definite activity of ZXX-4 was identified. Further in vivo assays in nude mice showed that ZXX-4 inhibited tumor proliferation in nude mice, reduced tumor metastasis, and enhanced the clinical efficacy of first-line drug sorafenib for the treatment of HCC. ZXX-4 can be further explored as an anti-tumor lead compound with a novel skeleton, and inhibition of thrombin can serve as a potential treatment strategy for HCC.

This article explores the connection between antioxidants and inflammatory immune-related diseases. It discusses the role of antioxidants in managing these diseases and highlights the potential health implications. The article includes summaries of studies on various diseases and their relationship to antioxidants, as well as the potential use of traditional Chinese Medicine and natural antioxidants in treatment. It also presents a study that found increased levels of retinol were associated with a reduced risk of ulcerative colitis. The document emphasizes the importance of antioxidants in treating these diseases and calls for further research in this area. [Extracted from the article]

Background: In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment. Methods: The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023. Results: Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion. Conclusion: By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients. [ABSTRACT FROM AUTHOR]

Bacterial infections are the second-leading cause of death, globally. The prevalence of antibacterial resistance has kept the demand strong for the development of new and potent drug candidates. It has been demonstrated that Src protein tyrosine kinases (TKs) play an important role in the regulation of inflammatory responses to tissue injury, which can trigger the onset of several severe diseases. We carried out a search for novel Src protein TK inhibitors, commencing from reported highly potent anti-bacterial compounds obtained using the Mannich reaction, using a combination of e-pharmacophore modeling, virtual screening, ensemble docking, and core hopping. The top-scoring compounds from ligand-based virtual screening were modified using protein structure-based design approaches, and their binding to the Src homology-2 domain of p56lck TK was predicted using ensemble molecular docking. We have prepared a database of 202 small molecules and have identified six novel top hits that can be subjected to further investigation. We have also performed in silico ADMET property prediction for the hit compounds. This combined computer-aided drug design approach can serve as a starting point for identifying novel TK inhibitors that could be further subjected to in vitro studies and validation of antimicrobial activity. [ABSTRACT FROM AUTHOR]

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target–cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure–activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold. [ABSTRACT FROM AUTHOR]

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. Scientific contribution: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease. [ABSTRACT FROM AUTHOR]

The inhibition of O-acetyl sulphydrylase synthase isoforms has been reported to represent a promising approach for the development of antibiotic adjuvants. This occurs via the organism developing an unpaired oxidative stress response, causing a reduction in antibiotic resistance in vegetative and swarm cell populations. This consequently increases the effectiveness of conventional antibiotics at lower doses. This study aimed to predict potential inhibitors of Salmonella typhimurium ortho acetyl sulphydrylase synthase (StOASS), which has lower binding energy than the cocrystalized ligand pyridoxal 5 phosphate (PLP), using a computer-aided drug design approach including pharmacophore modeling, virtual screening, and in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation. The screening and molecular docking of 4254 compounds obtained from the PubChem database were carried out using AutoDock vina, while a post-screening analysis was carried out using Discovery Studio. The best three hits were compounds with the PubChem IDs 118614633, 135715279, and 155773276, possessing binding affinities of −9.1, −8.9, and −8.8 kcal/mol, respectively. The in silico ADMET prediction showed that the pharmacokinetic properties of the best hits were relatively good. The optimization of the best three hits via scaffold hopping gave rise to 187 compounds, and they were docked against StOASS; this revealed that lead compound 1 had the lowest binding energy (−9.3 kcal/mol) and performed better than its parent compound 155773276. Lead compound 1, with the best binding affinity, has a hydroxyl group in its structure and a change in the core heterocycle of its parent compound to benzimidazole, and pyrimidine introduces a synergistic effect and consequently increases the binding energy. The stability of the best hit and optimized compound at the StOASS active site was determined using RMSD, RMSF, radius of gyration, and SASA plots generated from a molecular dynamics simulation. The MD simulation results were also used to monitor how the introduction of new functional groups of optimized compounds contributes to the stability of ligands at the target active site. The improved binding affinity of these compounds compared to PLP and their toxicity profile, which is predicted to be mild, highlights them as good inhibitors of StOASS, and hence, possible antimicrobial adjuvants. [ABSTRACT FROM AUTHOR]

Epidermal growth factor receptor (EGFR) and vascular endothelial growth factor 2 (VEGFR2) are known as valid targets for cancer therapy. Overexpression of EGFR induces uncontrolled cell proliferation and VEGF expression triggering angiogenesis via VEGFR2 signaling. On the other hand, VEGF expression independent of EGFR signaling is already known as one of the mechanisms of resistance to anti‐EGFR therapy. Therefore, drugs that act as dual inhibitors of EGFR and VEGFR2 can be a solution to the problem of drug resistance and increase the effectiveness of therapy. In this review, we summarize the relationship between EGFR and VEGFR2 signal transduction in promoting cancer growth and how their kinase domain structures can affect the selectivity of an inhibitor as the basis for designing dual inhibitors. In addition, several recent studies on the development of dual EGFR and VEGFR2 inhibitors involving docking simulations were highlighted in this paper to provide some references such as pharmacophore features of inhibitors and key residues for further research, especially in computer‐aided drug design. [ABSTRACT FROM AUTHOR]

1,2,3-Triazole-containing Nifedipine analogues offer the opportunity to increase biostability, bioavailability, efficacy and binding selectivity to target receptors. Here, we applied a computer-aided rational design for identifying new Nifedipine analogues containing a 1,2,3-triazole moiety. First, a new chemical library of 796 derivatives combining the 1,4-dihydropyridine (DHP) fragment and 1,2,3-triazole moiety was generated. Second, to reduce the library size, the library was pre-filtered using two 3D-pharmacophore models with different complexity, which allowed us to gradually reduce the chemical space, ending up with 26 hit candidates. Molecular docking calculations against the rCav1.1 receptor allowed the identification of eight derivatives 5a-h, characterized by the binding affinity towards the rCav1.1 receptor of the same level as approved Nifedipine-like drugs. Next, our molecular docking results were used to guide and optimize the retrosynthetic approaches for new analogues of Nifedipine as promising antihypertensive agents. So, a retrosynthetic approach for Nifedipine analogues with a 1,2,3-triazole ring in position 4 was proposed. Finally, eight analogues 5a-h determined by molecular docking calculations were synthesized using the suggested retrosynthetic approach. The aim of this study is to identify new Nifedipine analogues using a computer-aided drug design and a retrosynthetic approach. Materials and methods. The organic synthesis of new Nifedipine analogues containing a 1,2,3-triazole moiety. Computer-aided drug design of new DHP derivatives using pharmacophore screening and molecular docking calculations. Results. Molecular docking of new Nifedipine analogues made it possible to estimate the binding affinity of new Nifedipine derivatives to the rCav1.1 receptor. Pharmacophore screening of a chemical library of analogues, consisting of 796 derivatives, allowed gradually reducing the chemical space and obtaining 26 candidates with high affinity to the rCav1.1 receptor. Using the method of molecular docking, eight hits 5a-h were identified, and the synthesis of the recommended compounds was proposed and performed. Conclusions. The results of molecular docking showed that Nifedipine analogues 5a-h are characterized by binding affinity to the rCav1.1 receptor at the same level as approved Nifedipine-like drugs. Pharmacophore screening and molecular docking calculations indicate key features of the ligand-receptor interaction that can guide and optimize the synthesis of new Nifedipine analogues as promising new antihypertensive agents. A retrosynthetic approach was proposed, and the recommended compounds were synthesized [ABSTRACT FROM AUTHOR]

Introduction: The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field. [ABSTRACT FROM AUTHOR]

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure–activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (−11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment. [ABSTRACT FROM AUTHOR]

A tyrosine kinase receptor known as epidermal growth factor receptor (EGFR) is one of the main tumour markers in many cancer types and also plays a crucial role in cell proliferation, differentiation, angiogenesis, and apoptosis, which is a result of the auto-phosphorylations (kinase activity enhancement) that trigger signals involved in different cellular processes. Due to the discovery that non-small cell lung cancer (NSCLC) is a cause of this kinase activity enhancement, so far, several inhibitors have been tested against EGFR, but the side effects of these inhibitors necessitate an urgent measure to come up with an inhibitor that will be more specific to the cancer cells and not affect self-cells. This study was conducted to evaluate the efficacy of 37 compounds derived from Piper nigrum against EGFR using computer-aided drug design. Based on molecular docking, induced-fit docking, calculation of free binding energy, pharmacokinetics, QSAR prediction, and MD simulation. We propose five (5) lead compounds (clarkinol A, isodihydrofutoquinol B, Burchellin, kadsurin B, and lancifolin C) as a novel inhibitor, with clarkinol A demonstrating the highest binding affinity (−7.304 kcal/mol) with EGFR when compared with the standard drug (erlotinib). They also showed significant moderation for parameters investigated for a good pharmacokinetic profile, with a reliable R2 coefficient value predicted using QSAR models. The MD simulation of clarkinol A was found to be stable within the EGFR binding pocket throughout the 75 ns simulation run time. The findings showed that clarkinol A derived from Piper nigrum is worth further investigation and consideration as a possible EGFR inhibitor for the treatment of lung cancer. [ABSTRACT FROM AUTHOR]

Inhibitors of DNA methyltransferase (DNMTs) are now a major family of epigenetic targets with therapeutic interest. However, only two cytosine analogues 5-azacytosine (azacytidine) and 20-deoxy-5-azacytidine (decitabine), have been approved as the most cutting-edge medications for treating epigenetic cancer with some restrictions. In this context, computational methods that rely on quantitative structure-activity relationship (QSAR) play a crucial role allowing us to predict the biological activity of potential molecules based on the theoretically calculated physicochemical properties of these compounds. When coupled with machine learning (ML), QSAR approaches create an ideal platform for discovering potential drug candidates. In this study, three Machine Learning (ML) models; Random Forest, Support Vector Machine, and Artificial Neural Network, were trained using modified TeachOpenCADD KNIME workflows and applied it to the identification of plant molecules that are structurally similar to the active pharmaceuticals of current DNMT inhibitors. Then molecular docking simulations were performed using AutoDock Vina, employing two human DNMT structures (PDB codes: 4WXX and 2QRV) as target proteins and the predicted phytochemicals as ligands. Additionally, we focused on the R882H mutation hotspot in the catalytic domain of DNMT3A, which is associated with aberrant DNA methylation in acute myeloid leukemia (AML). Consequently, the structure of R882H DNMT3A (PDB code: 6W8J) was docked with the identified novel ligands. As a result of our computational analysis, eight phytochemicals were predicted as potential DNMT inhibitors through the ML approaches from KNIME. Subsequently, three of these phytochemicals, namely Herbacetin, Kaempferide, and Morin were identified as virtual hits against DNMTs following the molecular docking simulations. Overall, our study demonstrates the effectiveness of this computational strategy in identifying DNMT inhibitors. These findings hold promise for the discovery of potent and selective anticancer drugs targeting DNMTs. [ABSTRACT FROM AUTHOR]

The article discusses the use of computational methods in the design of anticancer drugs. Despite advancements in the development of new drugs, cancer remains a major health threat worldwide. Computational methods have become essential in the drug design process as they reduce costs and accelerate development. The article presents 11 contributions that cover various topics and targets in cancer treatment, including the development of inhibitors for specific proteins and the identification of potential drug candidates through virtual screening. The authors hope that these findings will inspire further research in this challenging field. [Extracted from the article]

Objectives: In our continuous efforts to combat COVID-19, our objective was to conduct a comprehensive computer-aided drug design study utilizing 4924 African natural metabolites sourced from diverse databases across various African regions from 1962 to 2019. The primary goal was to target the SARS-CoV-2 helicase, a crucial enzyme in viral replication. Methods: We employed structural fingerprint and molecular similarity studies with VXG, the co-crystallized ligand, as a reference. Subsequently, docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were conducted. Results: The structural fingerprint analysis identified 200 structurally similar compounds, then the molecular similarity studies selected 40 compounds. Among them, 15 metabolites with low free energies, favorable binding modes, and promising ADMET properties were identified. Four compounds were excluded according to the toxicity studies. Compound 1552, 1-((S)-2,3-dihydro-2-((R)-1-hydroxypropan-2-yl)benzofuran-5-yl)ethenone, exhibited the most favorable docking results. Molecular dynamics simulations conclusively demonstrated its stable binding to the SARS-CoV-2 helicase, characterized by low energy and optimal dynamics. Conclusion: The findings suggest promising avenues for potential COVID-19 cures, encouraging further exploration through in vitro and in vivo studies of the identified compounds, particularly compound 1552. [ABSTRACT FROM AUTHOR]