Multimodal Molecular Representation Learning with Graph Neural Networks, Deep&Cross Networks, and SMILES Embeddings
This work introduces a parameter-efficient Tri-Branch Modular Fusion Neural Network that synthesizes three orthogonal modalities: 3D spatial geometry, discrete topological grammar, and explicit macroscopic physicochemical descriptors that offers a highly efficient alternative to brute-force parameter scaling.