Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity.

ABSTRACT: Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation

SUBMITTER: Tamura S 

PROVIDER: S-EPMC9825040 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature