Details

Autor(en) / Beteiligte

• Klarich, Kathryn
• Goldman, Brian
• Kramer, Trevor
• Riley, Patrick
• Walters, W. Patrick

Titel

Thompson SamplingAn Efficient Method for Searching Ultralarge Synthesis on Demand Databases

Ist Teil von

• Journal of chemical information and modeling, 2024-02, Vol.64 (4), p.1158-1171

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• Over the last five years, virtual screening of ultralarge synthesis on-demand libraries has emerged as a powerful tool for hit identification in drug discovery programs. As these libraries have grown to tens of billions of molecules, we have reached a point where it is no longer cost-effective to screen every molecule virtually. To address these challenges, several groups have developed heuristic search methods to rapidly identify the best molecules on a virtual screen. This article describes the application of Thompson sampling (TS), an active learning approach that streamlines the virtual screening of large combinatorial libraries by performing a probabilistic search in the reagent space, thereby never requiring the full enumeration of the library. TS is a general technique that can be applied to various virtual screening modalities, including 2D and 3D similarity search, docking, and application of machine-learning models. In an illustrative example, we show that TS can identify more than half of the top 100 molecules from a docking-based virtual screen of 335 million molecules by evaluating 1% of the data set.