Molecular Docking: Shifting Paradigms in Drug Discovery

By Luca Pinzi, Giulio Rastelli. International Journal of Molecular Sciences, 2019

1,924 citations recorded by the source corpus.

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Subfields: Computational Theory and Mathematics, Organic Chemistry

View Paper: https://www.semanticscholar.org/paper/8f7948d72b19b3be7191396c5e637cdb14a2371c

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In Computational Theory and Mathematics

In Organic Chemistry

Citations in the corpus

Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management (2023, background): “These interactions can provide insights into the mechanism of action of the ligands and guide further optimization of their structure 19 .”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “Now molecular docking is being used in conjugation with MD simulation and AI-based tools in different areas of drug discovery like VS, target identification, polypharmacology, and drug repurposing [296].”
How accurately can one predict drug binding modes using AlphaFold models? (2023, methodology): “…gone into development of computational docking methods for predicting ligand-binding modes, which have dramatically accelerated this process in cases where a high-resolution experimentally determined structure of the target protein is available ( Ferreira et al., 2015 ; Pinzi and Rastelli, 2019 ).”