Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead Optimization.

ABSTRACT: Early evaluation of absorption, distribution, metabolism, and excretion (ADME) properties is crucial for streamlining drug development. Traditional in vivo and in vitro approaches are often expensive. Moreover, during lead optimization, these methods rely heavily on the expertise of specialists, leading to efficiency challenges. Consequently, in silico methods for ADME prediction are attracting increasing attention. However, existing in silico methods face two major issues: a decline in predictive performance caused by limited ADME data and a lack of clarity regarding the rationale for lead optimization to improve ADME properties. In this study, we built an AI model capable of predicting ten different ADME parameters to overcome these challenges. Our training approach was based on a graph

SUBMITTER: Ito S 

PROVIDER: S-EPMC12606635 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature