3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping.

ABSTRACT: Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in tar

SUBMITTER: Lo YC 

PROVIDER: S-EPMC5511031 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature