MolToxPred: small molecule toxicity prediction using machine learning approach.

ABSTRACT: Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and m

SUBMITTER: Setiya A 

PROVIDER: S-EPMC10826801 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature