De-risking drug discovery with predictive AI
A suite of new machine learning models can estimate the safety of potential new drugs
![An illustration depicting microchips and circuits displayed over promising drug molecules An illustration depicting microchips and circuits displayed over promising drug molecules](/https/www.broadinstitute.org/files/styles/multiple_ct_header_phone/public/longstory/Comms_ImagingPlatform_news_0524_v2.png?itok=Oh6JYWXB)
Funding
Support for these studies was provided by the National Institute of General Medical Sciences, the Cambridge Centre for Data-Driven Discovery, the Swedish Research Council, FORMAS, the Swedish Cancer Foundation, Horizon Europe, the Massachusetts Life Sciences Center, OASIS Consortium, and other sources.
Papers cited
Seal S, et al. Insights into drug cardiotoxicity from biological and chemical data: The first public classifiers for FDA drug-induced cardiotoxicity rank. Journal of Chemical Information and Modeling. Online February 1, 2024. DOI: 10.1021/acs.jcim.3c01834.
Seal S, et al. Improved detection of drug-induced liver injury by integrating predicted in vivo and in vitro data. Chemical Research in Toxicology. Online July 9, 2024. DOI: 10.1021/acs.chemrestox.4c00015.
Seal S, et al. From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability. Molecular Biology of the Cell. Online February 2, 2024. DOI: 10.1091/mbc.E23-08-0298.