Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge.

ABSTRACT: The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log P_tol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind

SUBMITTER: Draper MR 

PROVIDER: S-EPMC10938873 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature