Machine learning in chemoinformatics and drug discovery

By Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman. Drug Discovery Today, 2018

772 citations recorded by the source corpus.

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7b13474bf99b8fd20ee67cef5d76a9b298932ac4

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Citations in the corpus

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022, background): “Increasingly, modern drug discovery relies on customizable and target-speci ﬁ c machine learning-based quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR) modeling during virtual high-throughput screening (vHTS) (Lo et al., 2018; Vamathevan et al., 2019).”
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “…drug design (Ekins, 2016; Chen et al., 2018; Fleming, 2018), synthesis planning (Coley et al., 2018a), medicinal chemistry (Panteleev et al., 2018), cheminformatics (Lo et al., 2018), quantum mechanical calculations (Rupp, 2015), and materials science (Butler et al., 2018) have been collected.”
A Structure-Based Drug Discovery Paradigm (2019, background): “Structure prediction of biological targets (protein structure, binding pocket, transmembrane regions, and phosphorylation and glycosylation sites) and quantitative structure–activity relationship (QSAR) models, pharmacophore models, molecular docking analysis, and ranking/scoring functions in simila”
Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, background): “In [9], the incorporation of these models in drug prescription can save doctors and o ﬀ er new medical opportunities in pathology identiﬁcation.”
Machine Learning Methods in Drug Discovery (2020, methodology): “ML algorithms have been used to develop various models for predicting chemical, biological, and physical characteristics of compounds in drug discovery [11–19].”