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In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost. [ABSTRACT FROM AUTHOR]

Background: Hepatitis C virus (HCV) is a global medical condition that causes several life-threatening chronic diseases in the liver. The conventional interferon-free treatment regimens are currently in use by a blend of direct-acting antiviral agents (DAAs) aiming at the viral NS3 protease. However, major concerns may be the issue of DAA-resistant HCV strains and the limited availability to the DAAs due to their high price. Due to this crisis, the developments of a new molecule with high potency as an NS3/4A protease inhibitor of the hepatitis-C virus remain a high priority for medical research. This study aimed to use in-silico methods to identify high potent molecule as an NS3/4A protease inhibitor and investigating the binding energy of the identified molecule in comparison with approved direct-acting antiviral agents (Telaprevir, Simeprevir, and Voxilaprevir) through molecular docking. Results: The model obtained by in-silico method have the following statistical records, coefficient of determination (r2) of 0.7704, cross-validation (q2LOO = 0.6914); external test set (r2(pred) = 0.7049) and Y-randomization assessment (cR2p = 0.7025). The results from the model were used to identify 12 new potential human HCV NS3/4A protease inhibitors, and it was observed that the identified molecule is well-fixed when docked with the receptor and was found to have the lowest binding energy of − 10.7, compared to approved direct-acting antiviral agents (Telaprevir, Simeprevir, and Voxilaprevir) with − 9.5, − 10.0, − 10.5 binding energy, respectively. Conclusion: The binding affinity (− 10.7) of the newly identified molecule docked with 3D structures of HCV NS3/4a protease/helicase (PDB ID: 4A92) was found to be better than that of Telaprevir, Simeprevir, and Voxilaprevir (approved direct-acting antiviral agents) which are − 9.5, − 10.0, and − 10.5, respectively. Hence, a novel molecule was identified showing high potency as HCV NS3/4a protease inhibitors. [ABSTRACT FROM AUTHOR]

P‐glycoprotein (P‐gp)/MDR‐1 plays a major role in the development of multidrug resistance (MDR) by pumping the chemotherapeutic drugs out of the cancer cells and reducing their efficacy. A number of P‐gp inhibitors were reported to reverse the MDR when co‐administered with chemotherapeutic drugs. Unfortunately, none has approved for clinical use due to toxicity issues. Some of the P‐gp inhibitors tested in the clinics are reported to have cross‐reactivity with CYP450 drug‐metabolizing enzymes, resulting in unpredictable pharmacokinetics and toxicity of co‐administered chemotherapeutic drugs. In this study, two piperine analogs (3 and 4) having lower cross‐reactivity with CYP3A4 drug‐metabolizing enzyme are identified as P‐glycoprotein (P‐gp) inhibitors through computational design, followed by synthesis and testing in MDR cancer cell lines over‐expressing P‐gp (KB ChR 8–5, SW480‐VCR, and HCT‐15). Both the analogs significantly increased the vincristine efficacy in MDR cancer cell lines at low micromole concentrations. Specifically, 3 caused complete reversal of vincristine resistance in KB ChR 8–5 cells and found to act as competitive inhibitor of P‐gp as well as potentiated the vincristine‐induced NF‐KB‐mediated apoptosis. Therefore, 3 ((2E,4E)‐1‐(6,7‐dimethoxy‐3,4‐dihydroisoquinolin‐2(1H)‐yl)‐5‐(4‐hydroxy‐3‐methoxyphenyl)penta‐2,4‐dien‐1‐one) can serve as a potential P‐gp inhibitor for in vivo investigations, to reverse multidrug resistance in cancer. [ABSTRACT FROM AUTHOR]

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third‐party developers. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is one of the most common causes of dementia. It is believed that the aggregation of short Aβ‐peptides to form oligomeric and protofibrillar amyloid assemblies plays a central role for disease‐relevant neurotoxicity. In recent years, passive immunotherapy has been introduced as a potential treatment strategy with anti‐amyloid antibodies binding to Aβ‐amyloids and inducing their subsequent degradation by the immune system. Although so far mostly unsuccessful in clinical studies, the high‐dosed application of the monoclonal antibody Aducanumab has shown therapeutic potential that might be attributed to its much greater affinity to Aβ‐aggregates vs monomeric Aβ‐peptides. In order to better understand how Aducanumab interacts with aggregated Aβ‐forms compared to monomers, we have generated structural model complexes based on the known structure of Aducanumab in complex with an Aβ2 − 7‐eptitope. Structural models of Aducanumab bound to full‐sequence Aβ1 − 40‐monomers, oligomers, protofilaments and mature fibrils were generated and investigated using extensive molecular dynamics simulations to characterize the flexibility and possible additional interactions. Indeed, an aggregate‐specific N‐terminal binding motif was found in case of Aducanumab binding to oligomers, protofilaments and fibrils that is located next to but not overlapping with the epitope binding site found in the crystal structure with Aβ2 − 7. Analysis of binding energetics indicates that this motif binds weaker than the epitope but likely contributes to Aducanumab's preference for aggregated Aβ‐species. The predicted aggregate‐specific binding motif could potentially serve as a basis to reengineer Aducanumab for further enhanced preference to bind Aβ‐aggregates vs monomers. [ABSTRACT FROM AUTHOR]

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design. [ABSTRACT FROM AUTHOR]

The Piroplasmida order comprises parasitic protozoa including the Theileria and Babesia species that are transmitted by vector ticks and can cause severe diseases in domestic and wild animals. Because of limited therapies and available drug resistance, the discovery of new, effective, and safer drugs for veterinary use is important. Mitogen-activated protein kinases (MAPK) are a group of serine-threonine protein kinases found in diverse species, including animals and protozoa that conduct vital cellular functions. Therefore, they have been at the centre of drug design studies for many years. Computer-aided structure-based drug design is a fast and effective way in drug discovery efforts to identify candidate compounds. In this study, we conducted comparative sequence analysis of MAPK proteins from the Theileria (T. annulata, T. parva., T. orientalis, and T. equi) and Babesia species (B. bigemina, B. microti, and B. bovis). Three-dimensional protein structures from relevant species (T. annulata and B. bovis) were modelled and compounds were screened for interaction. Results showed that the inhibitors designed for human use could also be potent against Prioplasmida MAPKs. Furthermore, the structural differences between Prioplasmida and mammalian MAPKs could be a way for researchers to better instigate selective drug design. [ABSTRACT FROM AUTHOR]

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4. [ABSTRACT FROM AUTHOR]

The heterodimer of DNA excision repair protein ERCC‐1 and DNA repair endonuclease XPF (ERCC1‐XPF) is a 5′–3′ structure‐specific endonuclease essential for the nucleotide excision repair (NER) pathway, and it is also involved in other DNA repair pathways. In cancer cells, ERCC1‐XPF plays a central role in repairing DNA damage induced by chemotherapeutics including platinum‐based and cross‐linking agents; thus, its inhibition is a promising strategy to enhance the effect of these therapies. In this study, we rationally modified the structure of F06, a small molecule inhibitor of the ERCC1‐XPF interaction (Molecular Pharmacology, 84, 2013 and 12), to improve its binding to the target. We followed a multi‐step computational approach to investigate potential modification sites of F06, rationally design and rank a library of analogues, and identify candidates for chemical synthesis and in vitro testing. Our top compound, B5, showed an improved half‐maximum inhibitory concentration (IC50) value of 0.49 µM for the inhibition of ERCC1‐XPF endonuclease activit, and lays the foundation for further testing and optimization. Also, the computational approach reported here can be used to develop DNA repair inhibitors targeting the ERCC1‐XPF complex. [ABSTRACT FROM AUTHOR]

In this study quantum mechanical methods were used to predict the solvation energies of a series of drug-like molecules both in water and in octanol, in the context of the SAMPL6 n-octanol/water partition coefficient challenge. In pharmaceutical design, n-octanol/water partition coefficient, LogP, describes the drug's hydrophobicity and membrane permeability, thus, a well-established theoretical method that rapidly determines the hydrophobicity of a drug, enables the progress of the drug design. In this study, the solvation free energies were obtained via six different methodologies (B3LYP, M06-2X and ωB97XD functionals with 6-311+G** and 6-31G* basis sets) by taking into account the environment implicitly; the methodology chosen (B3LYP/6-311+G**) was used later to evaluate ΔGsolv by using explicit water as solvent. We optimized each conformer in different solvents separately, our calculations have shown that the stability of the conformers is highly dependent on the solvent environment. We have compared implicitly and explicitly solvated systems, the interaction of one explicit water with drug-molecules at the proper location leads to the prediction of more accurate LogP values. [ABSTRACT FROM AUTHOR]

With the escalating costs in drug development, discovering new uses of approved drugs, i.e., drug repurposing, has attracted increasing interest. Spermidine and spermine are important polyamines for most cells and their biosynthesis are strictly regulated by the polyamine metabolic network. In cancerous cells and tumor environments, the concentrations of polyamines are much higher than in normal cells. During the synthesis of spermidine and spermine, an amino-propyl group is provided by decarboxylated S-adenosylmethionine, and the latter is generated from S-adenosylmethionine by AdoMetDC (AdoMet decarboxylase). Therefore, as a rate-limiting enzyme in the biosynthesis of spermidine and spermine, AdoMetDC has been an attractive drug target in cancer studies. In the last decades, many AdoMetDC inhibitors have been discovered, and several AdoMetDC inhibitors are under clinical trials, but unfortunately, none of them have been approved yet. To overcome the high costs in time and money for discovering de novo inhibitors, we set out to repurpose clinic drugs as AdoMetDC inhibitors. We used steric-clashes alleviating receptors (SCAR), a computer-aided drug discovery strategy developed by us recently for in silico screening. By combining computational screening and experimental validation, we successfully identified two approved drugs that have inhibitory potency on AdoMetDC's enzymatic activity. SCAR was previously shown to be suitable for the discovery of both covalent and non-covalent inhibitors, and this work further demonstrated the value of the SCAR strategy in drug repurposing. [ABSTRACT FROM AUTHOR]

• A systematic virtual screening framework is presented for small molecule drug discovery. • Machine learning-based models and computer-aided drug design techniques are integrated in the framework. • The binding site selectivity of drug molecules is considered during the screening process. • The dynamic binding process of drug molecules to the target protein is analyzed. Structure-based virtual screening of binding of candidate drug molecules is a topic of increasing interest in the discovery of small molecule drugs. As the same drug molecule may bind to different binding sites on a target protein, the binding site selectivity that is related to the binding tendency of candidate drug molecules to different binding sites after reaching the target protein need to be considered in sufficient details. In this work, a systematic and computer-aided virtual screening framework based on the binding site selectivity to screen candidate drug molecules in terms of their ability to bind on selected sites is presented. The framework integrates two machine learning (ML)-based models to predict the binding potential and binding selectivity to specific binding sites that are important for virtual screening of drug molecules. The details of the ML-based models together with the work-flow of the computer-aided virtual screening methods and the efficient and consistent integration of related drug design tools are presented. The applicability of this virtual screening framework is illustrated through a case study involving the screening for drug molecules as inhibitors to block the binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to angiotensin converting enzyme 2 (ACE2), which is the target protein. The case study results point to identification of new candidate inhibitors with better binding site selectivity than two known potential inhibitors, Nilotinib and SSAA09E2. [ABSTRACT FROM AUTHOR]

Due to the adverse environmental impacts of toxic heavy metal–based antifoulants, the screening of environmentally friendly antifoulants has become important for the development of marine antifouling technology. Compared with the traditional lengthy and costly screening method, computer-aided drug design (CADD) offers a promising and efficient solution that can accelerate the screening process of green antifoulants. In this study, we selected barnacle chitin synthase (CHS, an important enzyme for barnacle settlement and development) as the target protein for docking screening. Three CHS genes were identified in the barnacle Amphibalanus amphitrite , and their encoded proteins were found to share a conserved glycosyltransferase domain. Molecular docking of 31,561 marine natural products with AaCHSs revealed that zoanthamine alkaloids had the best binding affinity (−11.8 to −12.6 kcal/mol) to AaCHSs. Considering that the low abundance of zoanthamine alkaloids in marine organisms would limit their application as antifoulants, a marine fungal–derived natural product, mycoepoxydiene (MED), which has a similar chemical structure to zoanthamine alkaloids and the potential for large-scale production by fermentation, was selected and validated for stable binding to AaCHS2L2 using molecular docking and molecular dynamics simulations. Finally, the efficacy of MED in inhibiting cyprid settlement of A. amphitrite was confirmed by a bioassay that demonstrated an EC 50 of 1.97 μg/mL, suggesting its potential as an antifoulant candidate. Our research confirmed the reliability of using AaCHSs as antifouling targets and has provided insights for the efficient discovery of green antifoulants by CADD. • CADD has the potential to accelerate the screening of eco-friendly antifoulants. • Zoanthamine alkaloids exhibit superior binding affinity to barnacle chitin synthase. • Mycoepoxydiene shows efficacy (EC 50 : 1.97 μg/mL) as an antifoulant candidate. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Understanding the underlying mechanism driving CRC progression and identifying potential therapeutic drug targets are of utmost urgency. We previously utilized LC-MS-based proteomic profiling to identify proteins associated with postoperative progression in stage II/III CRC. Here, we revealed that proteasome subunit beta type-1 (PSMB1) is an independent predictor for postoperative progression in stage II/III CRC. Mechanistically, PSMB1 binds directly to onco-protein RAB34 and promotes its proteasome-dependent degradation, potentially leading to the inactivation of the MEK/ERK signaling pathway and inhibition of CRC progression. To further identify potential anticancer drugs, we screened a library of 2509 FDA-approved drugs using computer-aided drug design (CADD) and identified Kinetin as a potentiating agent for PSMB1. Functional assays confirmed that Kinetin enhanced the interaction between PSMB1 and RAB34, hence facilitated the degradation of RAB34 protein and decreased the MEK/ERK phosphorylation. Kinetin suppresses CRC progression in patient-derived xenograft (PDX) and liver metastasis models. Conclusively, our study identifies PSMB1 as a potential biomarker and therapeutic target for CRC, and Kinetin as an anticancer drug by enhancing proteasome-dependent onco-protein degradation. [Display omitted] • PSMB1 was a predictor for postoperative progression in stage II/III CRC. • PSMB1 inactivated the MEK/ERK signaling pathway and inhibited CRC progression. • Kinetin facilitated PSMB1-mediated RAB34 degradation. • Kinetin suppressed CRC progression in vivo models. [ABSTRACT FROM AUTHOR]

Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet’s biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.

Since the outbreak of SARS-CoV-2, numerous compounds against COVID-19 have been derived by computer-aided drug design (CADD) studies. They are valuable resources for the development of COVID-19 therapeutics. In this work, we reviewed these studies and analyzed 779 compounds against 16 target proteins from 181 CADD publications. We performed unified docking simulations and neck-to-neck comparison with the solved co-crystal structures. We computed their chemical features and classified these compounds, aiming to provide insights for subsequent drug design. Through detailed analyses, we recommended a batch of compounds that are worth further study. Moreover, we organized all the abundant data and constructed a freely available database, DrugDevCovid19, to facilitate the development of COVID-19 therapeutics.

The lack of medication to treat COVID-19 is still an obstacle that needs to be addressed by all possible scientific approaches. It is essential to design newer drugs with varied approaches. A receptor-binding domain (RBD) is a key part of SARS-CoV-2 virus, located on its surface, that allows it to dock to ACE2 receptors present on human cells, which is followed by admission of virus into cells, and thus infection is triggered. Specific receptor-binding domains on the spike protein play a pivotal role in binding to the receptor. In this regard, the in silico method plays an important role, as it is more rapid and cost effective than the trial and error methods using experimental studies. A combination of virtual screening, molecular docking, molecular simulations and machine learning techniques are applied on a library of natural compounds to identify ligands that show significant binding affinity at the hydrophobic pocket of the RBD. A list of ligands with high binding affinity was obtained using molecular docking and molecular dynamics (MD) simulations for protein–ligand complexes. Machine learning (ML) classification schemes have been applied to obtain features of ligands and important descriptors, which help in identification of better binding ligands. A plethora of descriptors were used for training the self-organizing map algorithm. The model brings out descriptors important for protein–ligand interactions.

Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.

Molecular-dynamics (MD) simulations are playing an increasingly important role in research into the modes of action of G-protein coupled receptors (GPCRs). In this field, MD simulations are unusually important as, because of the difficult experimental situation, they often offer the only opportunity to determine structural and mechanistic features in atomistic detail. Modern combinations of soft- and hardware have made MD simulations a powerful tool in GPCR research. This is important because GPCRs are targeted by approximately half of the drugs on the market, so that computer-aided drug design plays a major role in GPCR research.

One of well-established biological activities for chalcone derivatives is as acetylcholinesterase inhibitors, which can be developed for the therapy of Alzheimer’s disease. Assisted byretrospectively validated structure-based virtual screening (SBVS) protocol to identify potent acetylcholinesterase inhibitors, 80chalcone derivatives were designed and virtually screened. The F-measure value as the parameter of the predictive ability of the SBVS protocol developed in the research presented in this article was 0.413, which was considerably better than the original SBVS protocol (F-measure = 0.226). Among the screened chalcone derivatives two were selected as potential lead compounds to designpotent inhibitors for acetylcholinesterase: 3-[4-(benzyloxy)-3-methoxyphenyl]-1-(4-hydroxy-3-methoxyphenyl)prop-2-en-1-one(3k) and 3-[4-(benzyloxy)-3-methoxyphenyl]-1-(4-hydroxyphenyl)prop-2-en-1-one (4k).

Background: The investigation and knowledge of calcium handling mechanisms in the plasmodium has been considered as a potential biological target against malaria. Objective: This study deals with the evaluation of inhibitory activity of secondary metabolites of ethylacetate partitioned-fraction of Adansonia digitata stem bark extract on malaria-associated protein using in silico docking studies. Materials and methods: Molecular docking and virtual screening was performed to understand the mechanism of ligand binding and to identify potent calcium transporter inhibitors. The stem bark extracts of A. digitata contains rich sources of phytochemicals. The secondary metabolites were determined by HPLC-DAD and HRGC-MS analysis. The major chemical constituent present in the ethylacetate partitioned-fraction of A. digitata stem bark extract were examined for their antiplasmodial activity and were also involved in docking study. Results: The secondary metabolites, quercetin and apigenin inhibited the formation of β-hematin. The results showed that all the selected compounds in the A. digitata showed binding energy ranging between -6.5 kcal/mol and -7.1 kcal/mol. Among the two chemical constituents, apigenin has the highest docking score along with the highest number of hydrogen bonds formed when compared to quercetin. Analysis of the results suggests that apigenin and quercetin could act as an anti-malaria agent. Conclusion: Molecular docking analysis could lead to further development of potent calcium transporter inhibitors for the prevention and treatment of malaria and related conditions.

Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development., Competing Interests: There are no conflicts to declare., (Copyright © 2024 Tangporncharoen et al.)

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., (© 2024. The Author(s).)

Alpinia officinarum is a commonly used spice with proven folk uses in various traditional medicines. In the current study, six compounds were isolated from its rhizomes, compounds 1-3 were identified as diarylheptanoids, while 4-6 were identified as flavonoids and phenolic acids. The isolated compounds were subjected to virtual screening against α-glucosidase, butyrylcholinesterase (BChE), and acetylcholinesterase (AChE) enzymes to evaluate their potential antidiabetic and anti-Alzheimer's activities. Molecular docking and dynamics studies revealed that 3 exhibited a strong binding affinity to human a α- glucosidase crystal structure compared to acarbose. Furthermore, 2 and 5 demonstrated high potency against AChE. The virtual screening results were further supported by in vitro assays, which assessed the compounds' effects on α-glucosidase, cholinesterases, and their antioxidant activities. 5-Hydroxy-7-(4-hydroxy-3-methoxyphenyl)-1-phenylheptan-3-one (2) showed potent antioxidant effect in both ABTs and ORAC assays, while p -hydroxy cinnamic acid (6) was the most potent in the ORAC assay. In contrary, kaempferide (4) and galangin (5) showed the most potent effect in metal chelation assay. 5-Hydroxy-1,7-diphenylhepta-4,6-dien-3-one (3) and 6 revealed the most potent effect as α-glucosidase inhibitors where compound 3 showed more potent effect compared to acarbose. Galangin (5) revealed a higher selectivity to BChE, while 2 showed the most potent activity to (AChE).

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome and (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2 H )-one ( 1 ), aryl(or heteroaryl)glyoxal monohydrates ( 2a - h ), and hydrazine monohydrate (NH 2 NH 2 •H 2 O) for the regioselective preparation of some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[ h ]cinnoline derivatives ( 3a - h ). After synthesis and characterization of the mentioned cinnolines ( 3a - h ), the in silico multi-targeting inhibitory properties of these heterocyclic scaffolds have been investigated upon various Homo sapiens -type enzymes, including h MAO-A, h MAO-B, h AChE, h BChE, h BACE-1, h BACE-2, h NQO-1, h NQO-2, h nNOS, h iNOS, h PARP-1, h PARP-2, h LRRK-2 (G2019S) , h GSK-3β, h p38α MAPK, h JNK-3, h OGA, h NMDA receptor, h nSMase-2, h IDO-1, h COMT, h LIMK-1, h LIMK-2, h RIPK-1, h UCH-L1, h PARK-7, and h DHODH, which have confirmed their functions and roles in the neurodegenerative diseases (NDs), based on molecular docking studies, and the obtained results were compared with a wide range of approved drugs and well-known (with IC 50 , EC 50 , etc.) compounds. In addition, in silico ADMET prediction analysis was performed to examine the prospective drug properties of the synthesized heterocyclic compounds ( 3a - h ). The obtained results from the molecular docking studies and ADMET-related data demonstrated that these series of 3-aryl(or heteroaryl)-5,6-dihydrobenzo[ h ]cinnolines ( 3a - h ), especially hit ones, can really be turned into the potent core of new drugs for the treatment of neurodegenerative diseases (NDs), and/or due to the having some reactionable locations, they are able to have further organic reactions (such as cross-coupling reactions), and expansion of these compounds (for example, with using other types of aryl(or heteroaryl)glyoxal monohydrates) makes a new avenue for designing novel and efficient drugs for this purpose.

Various in silico approaches to predict activity and properties of chemical compounds constitute nowadays the basis of computer-aided drug design. While there is a general focus on the predictions of values, mathematically more appropriate is the prognosis of probability distributions, which offers additional possibilities, such as the evaluation of uncertainty, higher moments, and quantiles. In this study, we applied the Hierarchical Correlation Reconstruction approach to assess several ADMET properties of chemical compounds. It uses multiple linear regression to independently assess multiple moments, which are then finally combined into predicted probability distribution. The method enables inexpensive selection of compounds with properties nearly certain to fall into the particular range during virtual screening and automatic rejection of predictions characterized by high rate of uncertainty; however, unlike to the currently used virtual screening methods, it focuses on the prediction of the property distribution, not its actual value. Moreover, the presented protocol enables detection of structural features, which should be carefully considered when optimizing compounds towards particular property, as well as it provides deeper understanding of the examined compound representations., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Phytochemicals are important chemical compounds in pharmaceutical chemistry. These natural compounds have interesting biological activities, including anticancer, as well as many other functions. EGFR (epidermal growth factor receptor) tyrosine kinase inhibition is emerging as one of the accepted methods in the treatment of cancer. On the other hand, computer-aided drug design has become an increasingly important field of study due to its many important advantages such as efficient use of time and other resources. In this study, fourteen phytochemicals which have triterpenoid structure and have recently entered the literature were investigated computationally for their potential as EGFR tyrosine kinase inhibitors. In the study, DFT (density functional theory) calculations, molecular docking, molecular dynamics simulations, binding free energy calculations with the use of MM-PBSA (molecular mechanics Poisson-Boltzmann Surface Area) method, and ADMET predictions were performed. The obtained results were compared to the results obtained for reference drug Gefitinib. Results showed that the investigated natural compounds are promising structures for EGFR tyrosine kinase inhibition.Communicated by Ramaswamy H. Sarma.

Introduction: Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments., Areas Covered: In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis., Expert Opinion: These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

Based on computer-aided drug design (CADD), the active groups of the known active small molecule compounds that can bind to EGFR target protein were analyzed through the molecular docking method. Then, 12 novel asiatic acid derivatives were synthesized by introducing active groups at ring A and C-28 positions of asiatic acid. The structures of these novel compounds were determined by NMR and MS. Furthermore, the anti-tumor activities of these derivatives on human lung cancer cells (A549) and human breast cancer cells (MCF-7) were evaluated by MTT assay. In conclusion, compounds I 4 and II 3 have stronger anti-cancer activity than parent compounds, the activities were stronger than gefitinib and comparable to afatinib, which may be potential candidate compounds for tumor therapy.

As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.

Lupus Nephritis (LN) is an autoimmune disease affecting the kidneys, and conventional drug studies have limitations due to its imprecise and complex pathogenesis. Therefore, the aim of this study was to design a novel Lupus Nephritis-targeted drug with good clinical due potential, high potency and selectivity by computer-assisted approach.NIK belongs to the serine/threonine protein kinase, which is gaining attention as a drug target for Lupus Nephritis. we used bioinformatics, homology modelling and sequence comparison analysis, small molecule ab initio design, ADMET analysis, molecular docking, molecular dynamics simulation, and MM/PBSA analysis to design and explore the selectivity and efficiency of a novel Lupus Nephritis-targeting drug, ClImYnib, and a classical NIK inhibitor, NIK SMI1. We used bioinformatics techniques to determine the correlation between lupus nephritis and the NF-κB signaling pathway. De novo drugs design was used to create a NIK-targeted inhibitor, ClImYnib, with lower toxicity, after which we used molecular dynamics to simulate NIK SMI1 against ClImYnib, and the simulation results showed that ClImYnib had better selectivity and efficiency. Our research delves into the molecular mechanism of protein ligands, and we have designed and validated an excellent NIK inhibitor using multiple computational simulation methods. More importantly, it provides an idea of target designing small molecules.Communicated by Ramaswamy H. Sarma.

Human norovirus is the leading cause of acute gastroenteritis worldwide, affecting every year 685 million people. Norovirus outbreaks are associated with very significant economic losses, with an estimated societal cost of 60 billion USD per year. Despite this, no therapeutic options or vaccines are currently available to treat or prevent this infection. An antiviral therapy that can be used as treatment and as a prophylactic measure in the case of outbreaks is urgently needed. We previously described the computer-aided design and synthesis of novel small-molecule agents able to inhibit the replication of human norovirus in cell-based systems. These compounds are non-nucleoside inhibitors of the viral polymerase and are characterized by a terminal para-substituted phenyl group connected to a central phenyl ring by an amide-thioamide linker, and a terminal thiophene ring. Here we describe new modifications of these scaffolds focused on exploring the role of the substituent at the para position of the terminal phenyl ring and on removing the thioamide portion of the amide-thioamide linker, to further explore structure-activity relationships (SARs) and improve antiviral properties. According to three to four-step synthetic routes, we prepared thirty novel compounds, which were then evaluated against the replication of both murine (MNV) and human (HuNoV) norovirus in cells. Derivatives in which the terminal phenyl group has been replaced by an unsubstituted benzoxazole or indole, and the thioamide component of the amide-thioamide linker has been removed, showed promising results in inhibiting HuNoV replication at low micromolar concentrations. Particularly, compound 28 was found to have an EC50 against HuNoV of 0.9 µM. Although the most active novel derivatives were also associated with an increased cytotoxicity in the human cell line, these compounds represent a very promising starting point for the development of new analogues with reduced cytotoxicity and improved selectivity indexes. In addition, the experimental biological data have been used to create an initial 3D quantitative structure-activity relationship model, which could be used to guide the future design of novel potential anti-norovirus agents.

Cervical cancer (CC) is the fourth most common pathology in women worldwide and presents a high impact in developing countries due to limited financial resources as well as difficulties in monitoring and access to health services. Human papillomavirus (HPV) is the leading cause of CC, and despite the approval of prophylactic vaccines, there is no effective treatment for patients with pre-existing infections or HPV-induced carcinomas. High-risk (HR) HPV E6 and E7 oncoproteins are considered biomarkers in CC progression. Since the E6 structure was resolved, it has been one of the most studied targets to develop novel and specific therapeutics to treat/manage CC. Therefore, several small molecules (plant-derived or synthetic compounds) have been reported as blockers/inhibitors of E6 oncoprotein action, and computational-aided methods have been of high relevance in their discovery and development. In silico approaches have become a powerful tool for reducing the time and cost of the drug development process. Thus, this review will depict small molecules that are already being explored as HR HPV E6 protein blockers and in silico approaches to the design of novel therapeutics for managing CC. Besides, future perspectives in CC therapy will be briefly discussed.

DNA methylation, an epigenetic modification, is mediated by DNA methyltransferases (DNMTs), a family of enzymes. Inhibitions of these enzymes are considered a promising strategy for the treatment of several diseases. In this study, a quantitative structure-activity relationship (QSAR) modeling was employed to understand the structure-activity relationship (SAR) of currently available non-nucleoside DNMT1 inhibitors (i.e., indole and oxazoline/1,2-oxazole scaffolds). Two QSAR models were successfully constructed using multiple linear regression (MLR) and provided good predictive performance (R2 Tr = 0.850-0.988 and R² CV = 0.672-0.869). Bond information content index (BIC1) and electronegativity (R6e+) are the most influential descriptors governing the activity of compounds. The constructed QSAR models were further applied for guiding a rational design of novel inhibitors. A novel set of 153 structurally modified compounds were designed in silico according to the important descriptors deduced from the QSAR finding, and their DNMT1 inhibitory activities were predicted. This result demonstrated that 86 newly designed inhibitors were predicted to elicit enhanced DNMT1 inhibitory activity when compared to their parent compounds. Finally, a set of promising compounds as potent DNMT1 inhibitors were highlighted to be further developed. The key SAR findings may also be beneficial for structural optimization to improve properties of the known inhibitors. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. Results: A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range −10.8 to −11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. Conclusion: The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone. [ABSTRACT FROM AUTHOR]

There are over 420 human solute carrier (SLC) transporters from 65 families that are expressed ubiquitously in the body. The SLCs mediate the movement of ions, drugs, and metabolites across membranes and their dysfunction has been associated with a variety of diseases, such as diabetes, cancer, and central nervous system (CNS) disorders. Thus, SLCs are emerging as important targets for therapeutic intervention. Recent technological advances in experimental and computational biology allow better characterization of SLC pharmacology. Here we describe recent approaches to modulate SLC transporter function, with an emphasis on the use of computational approaches and computer-aided drug design (CADD) to study nutrient transporters. Finally, we discuss future perspectives in the rational design of SLC drugs. Solute carrier transporters (SLCs) are biomedically important druggable targets but only a few SLCs are targeted by clinically approved drugs. Computer-aided drug design (CADD) has shown promise in the development of tool compounds for SLC transporters. Challenges in CADD for SLCs include the limited number of atomic resolution structures and small-molecule ligands for the development of accurate predictive models. Advances in computational power and machine learning will be powerful in the characterization of SLCs and the design of modulators. [ABSTRACT FROM AUTHOR]

Pancreatic adenocarcinoma (PC) is a lethal malignancy that is familial or associated with genetic syndromes in 10% of cases. Gene-based surveillance strategies for at-risk individuals may improve clinical outcomes. However, familial PC (FPC) is plagued by genetic heterogeneity and the genetic basis for the majority of FPC remains elusive, hampering the development of gene-based surveillance programs. The study was powered to identify genes with a cumulative pathogenic variant prevalence of at least 3%, which includes the most prevalent PC susceptibility gene, BRCA2. Since the majority of known PC susceptibility genes are involved in DNA repair, we focused on genes implicated in these pathways. We performed a region-based association study using the Mixed-Effects Score Test, followed by leave-one-out characterization of PC-associated gene regions and variants to identify the genes and variants driving risk associations. We evaluated 398 cases from two case series and 987 controls without a personal history of cancer. The first case series consisted of 109 patients with either FPC (n = 101) or PC at ≤50 years of age (n = 8). The second case series was composed of 289 unselected PC cases. We validated this discovery strategy by identifying known pathogenic BRCA2 variants, and also identified SMG1, encoding a serine/threonine protein kinase, to be significantly associated with PC following correction for multiple testing (p = 3.22x10-7). The SMG1 association was validated in a second independent series of 532 FPC cases and 753 controls (p<0.0062, OR = 1.88, 95%CI 1.17–3.03). We showed segregation of the c.4249A>G SMG1 variant in 3 affected relatives in a FPC kindred, and we found c.103G>A to be a recurrent SMG1 variant associating with PC in both the discovery and validation series. These results suggest that SMG1 is a novel PC susceptibility gene, and we identified specific SMG1 gene variants associated with PC risk. [ABSTRACT FROM AUTHOR]