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arXiv Machine Learning By Zhichao Deng, Haichuan Tan, Xin Hong, Huaizhi Wang, Tao Huang, Bowen Gao, Yanyan Lan

Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark

Read the original on arXiv Machine Learning →

Reverse target fishing seeks protein targets for a query molecule, a key step in drug discovery. The authors reframe this as a generative retrieval problem, introducing GenFish, which uses residual vector quantization to map protein language model features to shared semantic identifiers and autoregressively predicts protein codes from a molecular fingerprint. They also present ProTargetBench, a proteome‑scale benchmark covering 20,431 human proteins that distinguishes experimentally inactive from unmeasured associations, and show that GenFish outperforms both discriminative neural and non‑neural baselines on full‑catalogue retrieval metrics.

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 Machine Learning.

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