Co-authored by our CDSO Layla Hosseini-Gerami, Chemical Research in Toxicology has published Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.
This is a landmark collaboration involving 33 scientists from pharma, academia, and government that has produced a strategic framework for integrating AI into toxicology, offering a practical blueprint to enhance safety assessments and align with FDA goals for more human-relevant drug development.
At Ignota Labs we put this strategic framework into practice in our AI platform SAFEPATH, which combines cheminformatics, bioinformatics, and multimodal data analysis to explain why toxicities occur and how to mitigate them.
Read more
‘Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World,’ can be read in full in Chemical Research in Toxicology .
Authors: Srijit Seal*, Manas Mahale, Miguel García-Ortegón, Chaitanya K. Joshi, Layla Hosseini-Gerami, Alex Beatson, Matthew Greenig, Mrinal Shekhar, Arijit Patra, Caroline Weis, Arash Mehrjou, Adrien Badré, Brianna Paisley, Rhiannon Lowe, Shantanu Singh, Falgun Shah, Bjarki Johannesson, Dominic Williams, David Rouquie, Djork-Arné Clevert, Patrick Schwab, Nicola Richmond, Christos A. Nicolaou, Raymond J. Gonzalez, Russell Naven, Carolin Schramm, Lewis R Vidler, Kamel Mansouri, W. Patrick Walters, Deidre Dalmas Wilk, Ola Spjuth*, Anne E. Carpenter*, Andreas Bender*
Published: 2 May 2025 by Chemical Research in Toxicology.
