(140f) Leveraging Deep Learning for Pharmaceutical Discovery Lead Profiling
AIChE Annual Meeting
Tuesday, October 30, 2018 - 12:30pm to 12:55pm
This work describes the development of a model that can predict CYP inhibition from chemical structure using in-house data from BMS. Such quantitative structure activity relationship (QSAR) models have been successfully developed from publicly available data sets, however, the limited amount of public CYP inhibition data hinders their practical utility. As part of an innovative multidisciplinary team at BMS, analytics experts from different functional areas contributed to exploring and analyzing the BMS CYP assay database. Using this much larger set of private data (>1M entries) combined with recent advancements in machine learning algorithms, a model suitable for virtual screening was developed. Of the many modeling approaches that were explored, >85% classification accuracy was achieved using a deep learning approach. Important caveats for handling real-world data as well as a comparison of different modeling methodologies will be presented.