AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

ABSTRACT: Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that p

SUBMITTER: Schor J 

PROVIDER: S-EPMC9487703 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature