ArticleJournal of computer-aided molecular design2026
MutiDTAGen: fusion framework of perceptual new drug generation and drug-target affinity prediction through multi-scale feature extraction.
Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The conventional separation of drug-target affinity (DTA) prediction and de novo molecule generation creates a significant bottleneck in drug discovery. To address this, we introduce MutiDTAGen, a unified multi-task learning framework that establishes a bidirectional system between these two complementary tasks. By utilizing shared deep representations and a dynamic optimization strategy, the proposed framework ensures that knowledge from affinity prediction directly guides the generation of target-specific molecules. Our method demonstrates improved performance across multiple benchmarks, achieving, for instance, a 12% reduction in Mean Squared Error (MSE) on the Davis dataset compared to the GraphDTA baseline. This synergistic approach not only enhances prediction accuracy but also improves the quality and target-specificity of generated compounds. By unifying prediction and generation within a single end-to-end architecture, this study offers a unified and efficient computational strategy for drug discovery.
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