Computational approaches streamlining drug discovery

By Anastasiia V. Sadybekov, Vsevolod Katritch. 2023

985 citations recorded by the source corpus.

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. Recent advances in computational approaches and challenges in their application to streamlining drug discovery are discussed.

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

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Citations in the corpus

• State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, neutral): “…modulated by small molecules such as ligands and posttranslational modifications 13–16 ; such dynamical conformational changes can trigger diverse downstream responses that are crucial to the regulation of biological functions and with far-reaching implications for drug discovery 14,17–19 .”
• Artificial intelligence in drug development (2025, neutral): “Further, AI drug development faces talent shortages and investment risks due to long cycles, low success rates and uncertain returns, affecting investor confidence.”
• The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “Moreover, AI is shifting the paradigm of traditional drug discovery from screening large libraries of molecules to generating novel drug molecules from scratch [48].”
• Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease (2025, unknown): “Integration of DL models in the process can potentially increase the speed of the identification of candidate compounds during screening by many folds [90].”