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Molecular property prediction has evolved toward multimodal approaches that integrate three-dimensional geometry, SMILES-based topological representations, and macroscopic physicochemical descriptors. This synergy overcomes the limitations of traditional graph neural networks (GNNs), which often suffer from oversmoothing and difficulties capturing long-range dependencies. Solutions such as late fusion of orthogonal branches —inspired by architectures like SchNet, ChemBERTa, and Deep & Cross Networks— demonstrate that combining disparate information sources reduces mean absolute error below the chemical accuracy threshold, even with fewer than one million parameters. This breakthrough opens the door to high-throughput virtual screening (HTVS) in drug and materials discovery, where computational efficiency is critical.
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