Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations.

ABSTRACT: Explainable machine learning for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used; Morgan fingerprints and pre-trained SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by the area under the Receiver Operator Characteristic curve and balanced accuracy. In particular, pre-trained molecular SMILES embeddings as input to the mult

SUBMITTER: Sharma B 

PROVIDER: S-EPMC10039880 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature