To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.

Over half of cancer patients experience long-term pain management challenges.

Our setup is further extended to hybrid logical-neural constraints for capturing very complexconstraints,likegraphreachability.

During optimization, contrastive learning keeps the different modalities separated by a certain distance, which is influenced by the temperature parameter in the loss function. Our experiments further demonstrate that varying the modality gap distance has a significant impact in improving the model's downstream zero-shot classification performance and fairness.

Large-scale simulation models of complex socio-technical systems provide decision-makerswith high-fidelity testbeds inwhich policyinterventions canbe evaluated andwhat-if scenarios explored.