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A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design - Nature Machine Intelligence
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Generative models for structure-based molecular design hold considerable promise for drug discovery, with the potential to speed up the hit-to-lead development cycle while improving the quality of drug candidates and reducing costs. Data sparsity and bias are, however, the two main roadblocks to the development of three-dimensionally aware models. Here we propose a training protocol based on multilevel self-contrastive learning for improved bias control and data efficiency.
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