Ligand biological activity predicted by cleaning positive and negative chemical correlations.

ABSTRACT: Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chem

SUBMITTER: Lee AA 

PROVIDER: S-EPMC6397557 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature