ML-based toxic endpoint prediction
Determining the toxicity of compounds is vital to identify their harmful effects on humans, animals, plants and the environment. We focus on several aspects of machine learning (ML) in the context of toxicity prediction, e.g. investigating into novel descriptors, applicability of models to external data and interpretability of deep learning (DL) models (toxicophore). These investigations aim to come closer to the vision of transforming toxicology into a predictive science and reducing the number of animal testing.
These projects are part of the BMBF-funded BB3R initiative. One major goal of the BB3R initiative is the establishment of alternative methods for preclinical drug development and basic research.
KnowTox is a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of novel chemical entities, i.e. machine learning models, alerts for toxic substructures and computational support for read-across.
In this project, cytotoxicity prediction, one of the earliest handles in drug discovery, is investigated using a deep learning approach trained on a large and highly consistent in-house data set from the Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP). While neural networks are often described as a black box, we try to overcome the lack of interpretability. Thus, a Deep Taylor Decomposition method is investigated to identify substructures that may be responsible for the cytotoxic effects.