Activity Cliff-Informed Contrastive Learning for Molecular Property Prediction.

Modeling molecular activity and quantitative structure-activity relationships of chemical compounds is critical in drug design. Graph neural networks, which utilize molecular structures as frames, have shown success in assessing the biological activity of chemical compounds, guiding the selection and optimization of candidates for further development. However, current models often overlook activity cliffs (ACs)-cases where structurally similar molecules exhibit different bioactivities-due to latent spaces primarily optimized for structural features.

Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.

Drug discovery AI datasets and benchmarks have not traditionally included single-cell analysis biomarkers. While benchmarking efforts in single-cell analysis have recently released collections of single-cell tasks, they have yet to comprehensively release datasets, models, and benchmarks that integrate a broad range of therapeutic discovery tasks with cell-type-specific biomarkers. Therapeutics Commons (TDC-2) presents datasets, tools, models, and benchmarks integrating cell-type-specific contextual features with ML tasks across therapeutics.