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Int J Mol Sci ; 22(15)2021 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-34360558


Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").

Algoritmos , Descoberta de Drogas , Ensaios de Triagem em Larga Escala/normas , Proteínas/química , Bibliotecas de Moléculas Pequenas/química , Bibliotecas de Moléculas Pequenas/farmacologia , Avaliação Pré-Clínica de Medicamentos , Ensaios de Triagem em Larga Escala/métodos , Humanos
Pharmaceuticals (Basel) ; 14(8)2021 Aug 11.
Artigo em Inglês | MEDLINE | ID: mdl-34451887


In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model ("Skin Doctor CP:Bio") obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.

Int J Mol Sci ; 21(10)2020 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-32438666


Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.

Descoberta de Drogas , Aprendizado de Máquina , Terapia de Alvo Molecular , Bases de Conhecimento , Reprodutibilidade dos Testes
J Chem Inf Model ; 60(6): 2858-2875, 2020 06 22.
Artigo em Inglês | MEDLINE | ID: mdl-32368908


A plethora of similarity-based, network-based, machine learning, docking and hybrid approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing guidance in early drug discovery. With the increasing maturity of target prediction methods, researchers have started to explore ways to expand their scope to more challenging molecules such as structurally complex natural products and macrocyclic small molecules. In this work, we systematically explore the capacity of an alignment-based approach to identify the targets of structurally complex small molecules (including large and flexible natural products and macrocyclic compounds) based on the similarity of their 3D molecular shape to noncomplex molecules (i.e., more conventional, "drug-like", synthetic compounds). For this analysis, query sets of 10 representative, structurally complex molecules were compiled for each of the 28 pharmaceutically relevant proteins. Subsequently, ROCS, a leading shape-based screening engine, was utilized to generate rank-ordered lists of the potential targets of the 28 × 10 queries according to the similarity of their 3D molecular shapes with those of compounds from a knowledge base of 272 640 noncomplex small molecules active on a total of 3642 different proteins. Four of the scores implemented in ROCS were explored for target ranking, with the TanimotoCombo score consistently outperforming all others. The score successfully recovered the targets of 30% and 41% of the 280 queries among the top-5 and top-20 positions, respectively. For 24 out of the 28 investigated targets (86%), the method correctly assigned the first rank (out of 3642) to the target of interest for at least one of the 10 queries. The shape-based target prediction approach showed remarkable robustness, with good success rates obtained even for compounds that are clearly distinct from any of the ligands present in the knowledge base. However, complex natural products and macrocyclic compounds proved to be challenging even with this approach, although cases of complete failure were recorded only for a small number of targets.

Produtos Biológicos , Descoberta de Drogas , Ligantes , Aprendizado de Máquina , Proteínas
Brief Bioinform ; 21(3): 791-802, 2020 05 21.
Artigo em Inglês | MEDLINE | ID: mdl-31220208


Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.

Proc Natl Acad Sci U S A ; 116(38): 19109-19115, 2019 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-31462495


Viral inhibitors, such as pleconaril and vapendavir, target conserved regions in the capsids of rhinoviruses (RVs) and enteroviruses (EVs) by binding to a hydrophobic pocket in viral capsid protein 1 (VP1). In resistant RVs and EVs, bulky residues in this pocket prevent their binding. However, recently developed pyrazolopyrimidines inhibit pleconaril-resistant RVs and EVs, and computational modeling has suggested that they also bind to the hydrophobic pocket in VP1. We studied the mechanism of inhibition of pleconaril-resistant RVs using RV-B5 (1 of the 7 naturally pleconaril-resistant rhinoviruses) and OBR-5-340, a bioavailable pyrazolopyrimidine with proven in vivo activity, and determined the 3D-structure of the protein-ligand complex to 3.6 Å with cryoelectron microscopy. Our data indicate that, similar to other capsid binders, OBR-5-340 induces thermostability and inhibits viral adsorption and uncoating. However, we found that OBR-5-340 attaches closer to the entrance of the pocket than most other capsid binders, whose viral complexes have been studied so far, showing only marginal overlaps of the attachment sites. Comparing the experimentally determined 3D structure with the control, RV-B5 incubated with solvent only and determined to 3.2 Å, revealed no gross conformational changes upon OBR-5-340 binding. The pocket of the naturally OBR-5-340-resistant RV-A89 likewise incubated with OBR-5-340 and solved to 2.9 Å was empty. Pyrazolopyrimidines have a rigid molecular scaffold and may thus be less affected by a loss of entropy upon binding. They interact with less-conserved regions than known capsid binders. Overall, pyrazolopyrimidines could be more suitable for the development of new, broadly active inhibitors.

Antivirais/metabolismo , Capsídeo/metabolismo , Microscopia Crioeletrônica/métodos , Farmacorresistência Viral , Oxidiazóis/farmacologia , Rhinovirus/metabolismo , Proteínas Virais/química , Antivirais/farmacologia , Sítios de Ligação , Capsídeo/efeitos dos fármacos , Capsídeo/ultraestrutura , Células HeLa , Humanos , Modelos Moleculares , Estrutura Molecular , Oxazóis , Infecções por Picornaviridae/tratamento farmacológico , Infecções por Picornaviridae/metabolismo , Infecções por Picornaviridae/virologia , Ligação Proteica , Conformação Proteica , Rhinovirus/efeitos dos fármacos , Rhinovirus/ultraestrutura , Relação Estrutura-Atividade , Proteínas Virais/genética , Proteínas Virais/metabolismo