Chemical representation learning for toxicity prediction
Jannis Born, Greta Markert, Nikita Janakarajan, Talia B. Kimber, Andrea Volkamer, María Rodríguez Martínez, Matteo Manica
Abstract
A chemical language model for molecular property prediction: it outperforms prior art, is validated on a large, proprietary toxicity dataset, reveals cytotoxic motifs through attention & uses two uncertainty techniques to improve model reliability.
Topics & Concepts
Chemical toxicityRepresentation (politics)Computer scienceProperty (philosophy)Artificial intelligenceReliability (semiconductor)Machine learningToxicityTraining setNatural language processingChemistryOrganic chemistryPhilosophyQuantum mechanicsEpistemologyPhysicsPoliticsPolitical scienceLawPower (physics)Computational Drug Discovery MethodsMachine Learning in Materials ScienceChemistry and Chemical Engineering