About this Research Topic
The field of drug discovery is experiencing a paradigm shift with the integration of Artificial Intelligence (AI) and Machine Learning (ML) into the prediction of Absorption, Distribution, Metabolism, Excretion (ADME) properties and toxicological liabilities. Initially, ADME/Toxicology models played a marginal role in compound design, primarily focusing on key endpoints directly related to toxicity and efficacy. As AI and ML tools have matured, they have significantly improved our ability to predict how substances will behave in biological systems, transitioning from mere academic pursuits to critical components in pharmaceutical development. Recent studies have showcased the enhanced accuracy and predictive power of these tools, marking a substantial upgrade from traditional, more linear methods.
This Research Topic aims to explore the optimal application of ML models in ADME/Toxicology to enhance drug development efficacy. The goal is to not only develop the most effective machine learning models for specific endpoints such as permeability and toxicity but also to better interpret ML predictions to facilitate modifications in molecule design. We aim to address questions around the selection of ML models that balance high predictive accuracy with practical utility in drug discovery. Furthermore, the research will examine methods to assess the reliability of predictions which is crucial for minimizing risk in drug development.
To gather further insights in improving predictive modeling within ADME/Tox studies, we welcome articles addressing, but not limited to, the following themes:
Development of refined ML models for specific ADME/T endpoints like permeability and hepatotoxicity.
Techniques for enhancing the interpretability of machine learning outputs to improve drug molecule design.
Strategies for assessing the reliability and uncertainty of ML predictions in ADME/T.
Methods for integrating multiple ADME/Toxicology predictions into a cohesive scoring system to prioritize compound synthesis.
This Research Topic aims to explore the optimal application of ML models in ADME/Toxicology to enhance drug development efficacy. The goal is to not only develop the most effective machine learning models for specific endpoints such as permeability and toxicity but also to better interpret ML predictions to facilitate modifications in molecule design. We aim to address questions around the selection of ML models that balance high predictive accuracy with practical utility in drug discovery. Furthermore, the research will examine methods to assess the reliability of predictions which is crucial for minimizing risk in drug development.
To gather further insights in improving predictive modeling within ADME/Tox studies, we welcome articles addressing, but not limited to, the following themes:
Development of refined ML models for specific ADME/T endpoints like permeability and hepatotoxicity.
Techniques for enhancing the interpretability of machine learning outputs to improve drug molecule design.
Strategies for assessing the reliability and uncertainty of ML predictions in ADME/T.
Methods for integrating multiple ADME/Toxicology predictions into a cohesive scoring system to prioritize compound synthesis.
Keywords: Machine Learning, Drug Discovery, ADME, Tox, New Compounds Discovery, Cheminformatics, Pharmacokinetics Modeling
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
