Machine-learning approaches in drug discovery: methods and applications.

By Antonio Lavecchia. Drug Discovery Today, 2015

693 citations recorded by the source corpus.

During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9bbf8ca712bca3e93c898aea56d021a0317a30c1

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Citations in the corpus

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design (2021, neutral): “2 The rapid development of ML methods for drug discovery 3,4 has resulted in a growing need for high-quality benchmarks to allow for these methods to be evaluated and compared.”
DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019, methodology): “These algorithms model the quantitative structure-activity relationships (QSAR) or discern pharmacophores of ligand compounds for virtual screening [9-12].”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “Once the feature space is defined, assorted machine learning methods can be established to perform the DTI prediction task [5, 6, 9, 13, 14, 78, 89, 102, 106, 112, 127– 178”
Medical big data: promise and challenges (2017, neutral): “Big data analysis exploits various algorithms of data mining, which can be defined as the automatic extraction of useful, often previously unknown information from large databases or datasets using advanced search techniques and algorithms to discover patterns and correlations in large pre-existing ”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “As a subset of AI, ML is a technique for coordinating VS for drug leads, which generally incorporates gathering a filtered set of compounds, containing known actives and inactive compounds to train a model [423, 424].”