An SE(3)-equivariant transformer encodes 3D protein-ligand interactions via contrastive learning for zero-shot virtual screening, and these embeddings condition a multimodal chemical language model to autoregressively generate target-specific molecules with favorable predicted binding properties.
arXiv preprint arXiv:2406.08961 , year =
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Structure-guided molecular design with contrastive 3D protein-ligand learning
An SE(3)-equivariant transformer encodes 3D protein-ligand interactions via contrastive learning for zero-shot virtual screening, and these embeddings condition a multimodal chemical language model to autoregressively generate target-specific molecules with favorable predicted binding properties.