Advancing Computational Chemical Toxicology by Interpretable Machine Learning
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in human. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict toxicity potentials of chemicals. Although the applications of ML and DL based computational models in chemicals toxicity predictions are attractive, many toxicity models are “black box” in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate domain knowledge of toxicity models. In our recent research, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledgebase frameworks in IML development, and mechanistic chemical toxicity modeling. We hope that the applications of IML can advance the computational chemical toxicology into a new stage that eventually identify new toxicants with clear toxicity mechanism explorations.
Hosted by Professor Jason Zhang
~Coffee/tea will be served prior to the lecture~