Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands.

ABSTRACT: The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP). This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of

SUBMITTER: Rataj K 

PROVIDER: S-EPMC6100008 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature