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arXiv AI By Yixiao Li, Yining Qian, Yefan Chen, Zenghui Chen, Jiayue Sun, Yuhai Zhao, Cheng Tan, An-Yang Lu

Chemical and geometric representation fidelity improves drug--target affinity prediction

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The paper introduces ReGeoDTA, a framework that preserves chemical heterogeneity and continuous geometric relationships in drug and protein representations to improve drug–target affinity prediction. Experiments on three benchmark datasets show that maintaining representation fidelity consistently enhances predictive accuracy across various DTA architectures, while degrading representations harms performance and cannot be recovered by more complex downstream models. The study highlights representation fidelity as a key upstream design principle for accurate and generalizable affinity prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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