Rapid traversal of vast chemical space using machine learning-guided docking screens.

ABSTRACT: The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classi

SUBMITTER: Luttens A 

PROVIDER: S-EPMC12021657 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature