Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments. 

/pdf/computational-approaches-streamlining-drug-discovery-550mqbrw.pdf

Computational approaches streamlining drug discovery

Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free and bound states, we unambiguously evidence that an explicit description of protein-ligand noncovalent interactions does not provide any advantage with respect to ligand or protein descriptors. Simple models, inferring binding affinities of test samples from that of the closest ligands or proteins in the training set, already exhibit good performances, suggesting that memorization largely dominates true learning in the deep neural networks. The current study suggests considering only noncovalent interactions while omitting their protein and ligand atomic environments. Removing all hidden biases probably requires much denser protein-ligand training matrices and a coordinated effort of the drug design community to solve the necessary protein-ligand structures.

/pdf/on-the-frustration-to-predict-binding-affinities-from-2gj5dibt.pdf

On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks.

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery. 

http://www.cell.com/article/S2666379122003494/pdf

Deep generative molecular design reshapes drug discovery

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.

/pdf/scoring-functions-for-protein-ligand-binding-affinity-1oq4m2mu.pdf

Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTThe (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning MethodsThereza A. Soares*Thereza A. SoaresDepartment of Chemistry, University of São Paulo, Ribeirão Preto 055508−090, BrazilHylleraas Centre for Quantum Molecular Sciences, University of Oslo, Oslo 0315, NorwayMore by Thereza A. Soareshttps://orcid.org/0000-0002-5891-6906, Ariane Nunes-AlvesAriane Nunes-AlvesInstitute of Chemistry, Technische Universität Berlin, Berlin 10623, GermanyMore by Ariane Nunes-Alves, Angelica MazzolariAngelica MazzolariDepartment of Pharmaceutical Sciences, University of Milan, Via Mangiagalli 25, Milan I-20133, ItalyMore by Angelica Mazzolarihttps://orcid.org/0000-0003-1352-1094, Fiorella RuggiuFiorella RuggiuInsitro Inc., 279 East Grand Avenue, South San Francisco 94080, California, United StatesMore by Fiorella Ruggiuhttps://orcid.org/0000-0001-7260-3255, Guo-Wei WeiGuo-Wei WeiDepartment of Mathematics, Michigan State University, East Lansing 48824, Michigan, United StatesMore by Guo-Wei Weihttps://orcid.org/0000-0002-5781-2937, and Kenneth MerzKenneth MerzDepartment of Chemistry, Michigan State University, East Lansing 48824, Michigan, United StatesMore by Kenneth Merzhttps://orcid.org/0000-0001-9139-5893Cite this: J. Chem. Inf. Model. 2022, 62, 22, 5317–5320Publication Date (Web):November 28, 2022Publication History Received9 November 2022Published online28 November 2022Published inissue 28 November 2022https://doi.org/10.1021/acs.jcim.2c01422Copyright © Published 2022 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views2498Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertscloseSUBJECTS:Algorithms,Bioinformatics and computational biology,Drug discovery,Molecules,Structure activity relationship Get e-Alerts 

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c01422

The (Re)-Evolution of Quantitative Structure-Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods

Predicting accurate protein-ligand binding affinity is important in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the deep convolutional and graph neural network based approaches, the model performance depends on the input data representation and suffers from distinct limitations. It is natural to combine complementary features and their inference from the individual models for better predictions. We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction. We demonstrate effectiveness of the proposed approach by performing experiments with the PDBBind 2016 dataset and its docking pose complexes. The results show that the proposed approach improves the overall prediction compared to the individual neural network models with greater computational efficiency than related biophysics based energy scoring functions. We also discuss the benefit of the proposed fusion inference with several example complexes. The software is made available as open source at this https URL.

Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

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Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.