AI model directly compares properties of potential new drugs

Journal of Cheminformatics (2023). DOI: 10.1186/s13321-023-00769-x”> Traditional and pairwise architectures. A Traditional molecular machine learning models take singular molecular inputs and predict absolute properties of molecules. Predicted property differences can be calculated by subtracting predicted values for two molecules. B Pairwise models train on differences in properties from pairs of molecules to directly predict property changes of molecular derivatizations. C Molecules are cross-merged to create pairs only after cross-validation splits to prevent the risk of data leakage during model evaluation. Therefore, every molecule in the dataset can only occur in pairs in the training or testing data, but not both. Credit: Journal of Cheminformatics (2023). DOI: 10.1186/s13321-023-00769-x

Biomedical engineers at Duke University have developed an AI platform that autonomously compares molecules and learns from their variations to anticipate property differences critical to discovering new pharmaceuticals. The platform provides researchers with a more accurate and efficient tool to help design therapeutics and other chemicals with useful properties.

The research was published on October 27 in the Journal of Cheminformatics.

Machine learning algorithms are increasingly used to study and predict the biological, chemical and physical properties of small molecules used in drug development and other material design tasks. These tools can help researchers understand the key “ADMET” properties of a molecule—how it’s absorbed, distributed, metabolized, excreted and its toxicity within the body. By understanding these different properties, researchers can identify molecules to develop new therapeutics that are safer and

Continue reading on Phys.org