Computational Drug Discovery Methods

Computational drug discovery applies mathematical models, molecular simulation, and machine learning to predict how small molecules interact with biological targets, replacing or augmenting costly wet-lab experiments at early stages of the drug development pipeline. Techniques like molecular docking estimate how tightly a candidate compound binds to a target protein, while quantitative structure-activity relationship (QSAR) modeling links a molecule's chemical properties to its likely biological effect, and virtual screening rapidly filters millions of candidates down to a tractable shortlist. A central challenge is accurately predicting not just binding affinity but the full pharmacokinetic profile of a compound — how it is absorbed, distributed, metabolized, and excreted in the body — since many molecules that look promising in silico fail for these reasons in practice. An active frontier involves polypharmacology, where researchers deliberately model a drug's interactions across multiple targets simultaneously, raising open questions about how to handle the combinatorial complexity and off-target effects that emerge from network-level views of disease.

Works

171,807

Total citations

3,097,629

Keywords

Molecular DockingVirtual ScreeningDrug Target IdentificationQSAR ModelingPharmacokineticsChemical Properties