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Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small‐, medium‐, and large‐step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large‐step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real‐life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large‐step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand‐based methods. We also showed that a machine‐learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements. [ABSTRACT FROM AUTHOR]

The superposition of small molecules is a standard technique in molecular modeling and for some more advanced in silico applications of drug discovery a critical prerequisite. The aims of superposing molecules are manifold. An assessment of the 3D similarity, an understanding of the SAR in a compound series, or ultimately an estimate of the likelihood of a compound to be active and selective against a target protein of interest. Considering so many objectives it is not surprising that new superpositioning methods are continuously developed and the overlay problem cannot be considered solved. We present 51 superposition methods with a focus on those published in the 21st century. For 36 methods that are currently available, we briefly describe and compare the respective pose generation and scoring processes. While the modeling community got a wealth of methods at hand, the scientific necessity of rigorous and comparable benchmarking becomes apparent. This article is categorized under:Data Science > ChemoinformaticsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

The availability of large chemical libraries containing hundreds of millions to billions of diverse drug‐like molecules combined with an almost unlimited amount of compute power to achieve scientific calculations has led investors and researchers to have a renewed interest in virtual screening (VS) methods to identify biologically active compounds. The number of in silico screening tools and software which employ the knowledge of the protein target or known bioactive ligands is increasing at a rapid pace, creating a crowded computational landscape where it has become difficult to assess the real advantages and disadvantages in terms of accuracy and efficiency of each individual VS technology. In the current work, we evaluate the performance of several state‐of‐the‐art commercial software for 3D ligand‐based VS against well‐known 2D methods using an internally curated benchmarking data set. Our results show that the best individual methods can differ significantly based on the data set, and that combining them using data fusion techniques results in improved enrichment in the top 1 % of retrieved hits. Although 2D methods alone can already provide a significant enrichment in the number of predicted active compounds, the combination of data‐fused 2D results with just one out of the best 3D methods (ROCS, FLAP or Blaze) further improves early enrichment and the likelihood of identifying additional chemotypes. [ABSTRACT FROM AUTHOR]

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligandbased classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity. [ABSTRACT FROM AUTHOR]

Background This work presents a novel computational multi-reference poly-conformational algorithm for design, optimization, and repositioning of pharmaceutical compounds. Methods The algorithm searches for candidates by comparing similarities between conformers of the same compound and identifies target compounds, whose conformers are collectively close to the conformers of each compound in the reference set. Reference compounds may possess highly variable MoAs, which directly, and simultaneously, shape the properties of target candidate compounds. Results The algorithm functionality has been case study validated in silico, by scoring ChEMBL drugs against FDA-approved reference compounds that either have the highest predicted binding affinity to our chosen SARS-CoV-2 targets or are confirmed to be inhibiting such targets in-vivo. All our top scoring ChEMBL compounds also turned out to be either high-affinity ligands to the chosen targets (as confirmed in separate studies) or show significant efficacy, in-vivo, against those selected targets. In addition to method case study validation, in silico search for new compounds within two virtual libraries from the Enamine database is presented. The library’s virtual compounds have been compared to the same set of reference drugs that we used for case study validation: Olaparib, Tadalafil, Ergotamine and Remdesivir. The large reference set of four potential SARS-CoV-2 compounds has been selected, since no drug has been identified to be 100% effective against the virus so far, possibly because each candidate drug was targeting only one, particular MoA. The goal here was to introduce a new methodology for identifying potential candidate(s) that cover multiple MoA-s presented within a set of reference compounds.

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

Inhibition of the human ether‐à‐go‐go‐related gene (hERG) ion channel may lead to prolonged QT interval and even fatal ventricular arrhythmia. Therefore, evaluating the hERG liability has become a mandatory requirement for drug development and drug safety process. However, standard experimental assays are complex, expensive and time‐consuming. Various in silico tools have been developed to identify potential hERG blockers and reduce in advance the risk of cardiotoxicity‐related attritions during the early drug discovery phase. In this review, we first outlined the structure and gating properties of the hERG channel briefly. Then analyzed the advantages of computational technology, and comprehensively summarized the in silico methods of hERG‐related cardiotoxicity, including ligand‐based and structure‐based methods. Additionally, the current predictive models in prediction of hERG blockage were emphatically reviewed. Finally, issues involved in the current computational methods, as well as the future direction for developing reliable models of hERG channel blockers, are discussed. [ABSTRACT FROM AUTHOR]

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the “repurposing” concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the “repurposing” concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the “repurposing” concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the “repurposing” concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material.

Graphical abstract Highlights • MDDR, DrugBank and TargetMol databases were consulted for drug repositioning. • Combined ligand-based and structure-based methods were effective strategies for VS. • The six compounds selected showed antifungal activity against Paracoccidioides spp. • The antiviral agent raltegravir showed promising in vivo antifungal activity. • The in vivo assay suggested that raltegravir may be an agent for PCM treatment. Abstract Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis. [ABSTRACT FROM AUTHOR]

One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.

We describe a ligand-based approach towards compounds with more specific targeting for Burkitt’s lymphoma. Using three-dimensional ligand-based similarity searches and a previously described hit compound, we have identified six compounds that are chemically different but with similar spatial conformations. Biological evaluation revealed that one compound has better growth inhibition and improved selectivity towards Burkitt’s lymphoma cells than the query compound. However, initial mechanism-of-action studies show a different target profile in comparison with the previous hit compound, which does not involve the inhibition of the proteasome or the NFκB pathway. The data from this study provide a solid basis for further efforts in the search for selective agents against Burkitt’s lymphoma.

Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see ). [ABSTRACT FROM AUTHOR]

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds. [ABSTRACT FROM AUTHOR]

In a previous work, we developed the in house Virtual Lock-and-Key Approach (VLKA) in order to evaluate target assignment starting from molecular descriptors calculated on known inhibitors used as an information source. This protocol was able to predict the correct biological target for the whole dataset with a good degree of reliability (80%), and proved experimentally, which was useful for the target fishing of unknown compounds. In this paper, we tried to remodel the previous in house developed VLKA in a more sophisticated one in order to evaluate the influence of 3D conformation of ligands on the accuracy of the prediction. We applied the same previous algorithm of scoring and ranking but, this time, combining it with a structure-based approach as docking. For this reason, we retrieved from the RCSB Protein Data Bank (PDB), the available 3D structures of the biological targets included into the previous work, and we used them to calculate poses of the 7352 dataset compounds in the VLKA biological targets. First, docking protocol has been used to retrieve docking scores, then, from the docked poses of each molecule, 3D-descriptors were calculated (Conf-VLKA), While the use of the simple docking scores proved to be inadequate to improve compounds classification, the Conf-VLKA showed some interesting variations compared to the original VLKA, especially for targets whose ligands present a high number of rotamers. This work represent a first preliminary study to be completed using other techniques such as induced fit docking or molecular dynamics structure clustering to take into account the protein side chains adaptation to ligands structures. [ABSTRACT FROM AUTHOR]

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological proile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands. [ABSTRACT FROM AUTHOR]

Ligand-based virtual screening is a well established method to find new lead molecules in todays drug discovery process. In order to be applicable in day to day practice, such methods have to face multiple challenges. The most important part is the reliability of the results, which can be shown and compared in retrospective studies. Furthermore, in the case of 3D methods, they need to provide biologically relevant molecular alignments of the ligands, that can be further investigated by a medicinal chemist. Last but not least, they have to be able to screen large databases in reasonable time. Many algorithms for ligand-based virtual screening have been proposed in the past, most of them based on pairwise comparisons. Here, a new method is introduced called mRAISE. Based on structural alignments, it uses a descriptor-based bitmap search engine (RAISE) to achieve efficiency. Alignments created on the fly by the search engine get evaluated with an independent shape-based scoring function also used for ranking of compounds. The correct ranking as well as the alignment quality of the method are evaluated and compared to other state of the art methods. On the commonly used Directory of Useful Decoys dataset mRAISE achieves an average area under the ROC curve of 0.76, an average enrichment factor at 1 % of 20.2 and an average hit rate at 1 % of 55.5. With these results, mRAISE is always among the top performing methods with available data for comparison. To access the quality of the alignments calculated by ligand-based virtual screening methods, we introduce a new dataset containing 180 prealigned ligands for 11 diverse targets. Within the top ten ranked conformations, the alignment closest to X-ray structure calculated with mRAISE has a root-mean-square deviation of less than 2.0 Å for 80.8 % of alignment pairs and achieves a median of less than 2.0 Å for eight of the 11 cases. The dataset used to rate the quality of the calculated alignments is freely available at . The table of all PDB codes contained in the ensembles can be found in the supplementary material. The software tool mRAISE is freely available for evaluation purposes and academic use (see ). [ABSTRACT FROM AUTHOR]

We report herein the generation and validation of a 3D-QSAR model based on a set of antimalarials previously described by us and characterized by a clotrimazole-based pharmacophore. A novel series of derivatives was synthesized and showed activity against Plasmodium falciparum chloroquine-sensitive (CQ-S) and chloroquine-resistant (CQ-R) strains. Gratifyingly, compounds 35a – c showed interesting activity against P. falciparum CQ-R strains with improved predicted physico-chemical properties. [ABSTRACT FROM AUTHOR]

One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure. [ABSTRACT FROM AUTHOR]

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision. [ABSTRACT FROM AUTHOR]

• Combined synthetic and natural compounds reported as Bcl-2 inhibitors were applied to generate pharmacophore and QSAR model. • ZINC database was screened to identify molecules with similar chemical features and predicted pIC50 from QSAR equation. • Docking, MD simulations with MM-PBSA calculations were performed to identify in silico potential Bcl-2 inhibitors. • Study identified ZINC07405677, ZINC41435991, ZINC71943283 and ZINC95430809 as potential hit molecules for cancer. The apoptotic balance of the cell is governed by Bcl-2 protein family. Increased expression of pro-survival members of Bcl-2 family is indicative of cell death evasion and cancer metastasis. The current therapeutic approach to regulate the Bcl-2 overexpression using small molecule, although effective, is limited by dose related toxicity. In the present study, a combination of features from synthetic drugs and phytochemicals are used to design drugs balances both safety and efficacy. In our approach, ligand-based pharmacophore model was generated using both the classes of drugs as input. Compounds with good fitness score and pharmacophore features were screened from ZINC database. The activity (pIC50) of the compounds were predicted on the basis of QSAR studies. From the analysis, four compounds ZINC07405677, ZINC41435991, ZINC71943283 and ZINC95430809 showed maximum activity with pIC50 of 17.6, 14.95,14.79 and 12.70 respectively. Given that pIC50 of 9.67 was the highest in literature reported compounds, it can be concluded that the ZINC compounds have demonstrated better Bcl-2 inhibition capacity in comparison. Further, docking, ADMET and molecular dynamics simulation studies against Bcl-2 helped in quantitatively assessing and confirming high activity, good stability and binding affinity of identified ZINC compounds. [ABSTRACT FROM AUTHOR]

Computer-aided drug design scaffold hopping strategies were utilized to identify new classes of CB2 agonists when compounds of an established series with low nanomolar potency were challenging to optimize for good drug-like properties. Use of ligand-based design strategies through BI Builder (a tool for de novo design) and PharmShape (a virtual screening software package) approaches led to the discovery of new chemotypes. Specifically, compounds containing azetidine-, proline-, and piperidine-based cores were found to have low nanomolar and picomolar CB2 agonist activities with drug-like properties considered appropriate for early profiling. [ABSTRACT FROM AUTHOR]

We describe a ligand-based approach towards compounds with more specific targeting for Burkitt's lymphoma. Using three-dimensional ligand-based similarity searches and a previously described hit compound, we have identified six compounds that are chemically different but with similar spatial conformations. Biological evaluation revealed that one compound has better growth inhibition and improved selectivity towards Burkitt's lymphoma cells than the query compound. However, initial mechanism-of-action studies show a different target profile in comparison with the previous hit compound, which does not involve the inhibition of the proteasome or the NFκB pathway. The data from this study provide a solid basis for further efforts in the search for selective agents against Burkitt's lymphoma. [ABSTRACT FROM AUTHOR]

Abstract: Monoamine oxidase B (MAO-B) inhibitors are used to treat Parkinson’s disease. In this study, we searched for novel MAO-B inhibitors using a scaffold hopping approach based on our experience with the thiazolidinedione (TZD) class of compounds as MAO-B inhibitors. Several novel compounds were identified, with potencies in the low nanomolar and low micromolar range. We also found that derivatives of the natural product sulfuretin are potent MAO-A and MAO-B inhibitors. [Copyright &y& Elsevier]

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material. [ABSTRACT FROM AUTHOR]

[Display omitted] The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications. [ABSTRACT FROM AUTHOR]

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analyzed for their effectiveness in virtual screening against FFAR2. To evaluate the performance of these methods, retrospective virtual screening was performed. Statistical quality of the methods was evaluated by BEDROC and RIE. The results revealed that electrostatic similarity search protocol using EON (ET combo) outperformed all other protocols with outstanding enrichment of >95% in top 1% and 2% of the dataset with an AUC of 0.958. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary to hits found from electrostatic similarity searching. These results suggest that considering electrostatic similarity searching first increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign. Accordingly, prospective virtual screening using electrostatic similarity searching was used to identify novel FFAR2 ligands. The discovered compounds provide new chemical matter starting points for the initiation of a medicinal chemistry campaign. [ABSTRACT FROM AUTHOR]

Constant research on natural products has generated, over time, a large number of compounds with the potential to be evaluated in several biological tests and subsequently have been cataloged in databases that allow other researchers to perform virtual screenings of activity in various biological systems. This considerably reduces the time for the development of new drugs. This review describes the main databases of natural products available for searching bioactive compounds. It also describes the main features of virtual screening strategies for the identification of molecules with the potential to be used as new drugs. In addition, a search was made in the Web of Science database, using the search term "Virtual screening of natural products databases" from 2003 to 2018. The search criterion resulted in 230 articles, which had their abstracts evaluated with pertinence to the criteria required for this work, which are: a) be a research article; b) performing a virtual screening on databases of natural products or containing natural products; and c) works that identified drug candidate molecules. Based on these criteria, the bibliographic review on the topic was excluded. After this analysis, 104 works were selected for this review. We selected relevant papers describing the potential drug candidates that were distributed in 15 classes, of which the anticancer, antibacterial and anti-inflammatory hits were the most abundant. The works showing efforts to search for new molecules against various other diseases in distinct biological systems were also described. In this way, this work shows an overview of several methodologies and we hope they can help and inspire the development of new research to improve people's quality of life., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Organocalcogens are a class of organic compounds obtained by the synthesis experiments to include S, Se, or Te. Among the elements that comprise this class, Se is characterized as an essential mineral and nutrient for humans. Se has been widely studied in many aspects. Organic synthesis of organoselenides is used for obtaining new potential drug candidates and may be highly beneficial from the use of computational approaches to reduce time and cost of the experiments. Thus, the goal of our study is to evaluate the computational approaches used in the organoselenides research from 1999 to 2019., Methods: A literature review was performed by searching the database "Web of Sciences"., Results: Most of the theoretical studies included structural elucidation or structure-property analysis. We also found research regarding molecular docking approaches and Quantitative Structure-Activity Relationship (QSAR) studies., Conclusions: Computational studies have been widely applied to organoselenides. They demonstrated promising results and resulted in reduced the cost of research, increased efficacy, and, ultimately, novel organoselenides with desired properties., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively., Competing Interests: Declaration of competing interest The authors declares no conflicts of interest., (Copyright © 2020 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.)

Introduction: Although the transition of a lead candidate into a drug is currently structured by well-defined milestone, it is still most challenging and offers no guarantee in success to the end. In fact, ligand-based pharmacophore modeling has become a key motive force for retrieving potential leads across several therapeutic areas., Methods: An urgent need towards the development of novel antidepressant agents led us to generate a pharmacophore model from an existing 44 compounds dataset. The best model with one hydrophobic, two ring aromatic, and one positive ionization features was chosen on behalf of the correlation coefficient, sensitivity, specificity, yield of actives and accuracy measures using HypoGen module of Discovery Studio. In house library consisting of 10,000 substituted 1,3,5 triazine derivatives were shortlisted to select four insilico hits. All shortlisted compounds were synthesized and characterized by FTIR, 1H-& 13C-NMR spectroscopy and finally tested for antidepressant-like activity using behavioral models on rats viz. Forced Swim Test (FST) and Elevated Plus Maze (EPM)., Results: Two shortlisted compounds with optimal fit values showed a significant decrease in the duration of immobility as compared to standard drug Imipramine in FST while time spent in open arm in enhanced in case of EPM., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on these scientific problems challenge many researchers to find a different way to reduce the extensive time and cost involved in the drug discovery cycle of these problems. Computer-aided drug design techniques have already been proved successful in the discovery of new molecules rationally by reducing the time and cost involved in the development of drugs. In the current minireview, we are highlighting on the molecular modeling studies published during 2010-2018 for target specific antitubercular agents. This review includes the studies of Structure-Based (SB) and Ligand-Based (LB) modeling and those involving Machine Learning (ML) techniques against different antitubercular targets such as dihydrofolate reductase (DHFR), enoyl Acyl Carrier Protein (ACP) reductase (InhA), catalase-peroxidase (KatG), enzyme antigen 85C, protein tyrosine phosphatases (PtpA and PtpB), dUTPase, thioredoxin reductase (MtTrxR), etc. The information presented in this review will help the researchers to get acquainted with the recent progress in the modeling studies of antitubercular agents., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Inflammation has been very evident in infectious diseases, but in recent times research has increasingly shown that a range of non-infectious diseases may present with inflammatory conditions. This fact becomes important as new anti-inflammatory drugs emerge with different targets for treatment of diseases. Virtual screening (VS) involves applying computational methods to discover new ligands for biological structures from the formation of large libraries composed of a large number of compounds. This review aims to report several studies employing a variety of VS: ligand-based and structure-based VS are being used more frequently in combination to decrease the probability of choosing false positive candidates. There are also studies that use only one approach. Docking is widely employed as structure-based VS methodology, however pharmacophore models based on the structure are becoming more prevalent. Molecular dynamics simulations, despite their computational cost, are still utilized to validate docking scores and analyze the stability of the complex ligand-structure. It is important to note that several studies employed several drug-like rules to screen structures, as well, decoys and PAINS to validate the models. Natural product databases, despite the lower number of the compounds compared to other databases that are available, are commonly referred to as a source of drug-like molecules. There is a literal explosion of software being released for a variety of purposes and several of them are free tools and/or web tools. Overall, VS studies are nowadays a normal part of medicinal chemistry to determine novel potential inhibitors for targets of inflammatory diseases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)