Generative retrieval improves protein target identification for drug molecules
Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark
Machine Learning
Summary
Finding which proteins a drug-like molecule interacts with is important for designing and reusing medicines. The authors developed GenFish, a new method that avoids wrongly assuming unknown interactions mean no connection. They trained it to predict protein targets by learning shared codes rather than labeling missing data as negative. This approach works better than previous methods on a large new benchmark covering over 20,000 human proteins. The results show a promising way to discover protein targets even when data about molecule-protein pairs is incomplete.
What this means in practice
- •For drug discovery teams: Identify protein targets for candidate molecules more accurately despite incomplete binding data to guide drug development and repurposing.
- •For bioinformatics platform developers: Integrate a generative retrieval system that ranks proteins by predicted interaction probability using learned semantic codes, improving large-scale target fishing.
Authors
Zhichao Deng, Haichuan Tan, Xin Hong, Huaizhi Wang, Tao Huang, Bowen Gao, Yanyan Lan
Abstract
Reverse target fishing identifies protein targets for a query molecule and is central to drug discovery and repurposing. Discriminative approaches adapted from virtual screening face sparse, long-tailed associations and missing-not-at-random measurements: treating unrecorded pairs as negatives can suppress genuine targets. We reformulate the task as generative retrieval and introduce GenFish. Residual vector quantization converts frozen protein language model features into shared semantic identifiers. Given a molecular fingerprint, GenFish autoregressively models codes conditioned on the query and preceding code, ranking proteins by identifier likelihood without explicitly labeling unrecorded pairs as negatives. We introduce ProTargetBench, the first proteome-scale benchmark for reverse target fishing to jointly support measured-panel discrimination and full-catalogue recovery of experimentally recorded positives. Covering 20,431 UniProt-reviewed human proteins, it integrates curated bioactivity evidence and distinguishes experimental inactivity from unmeasured associations. On 198 held-out molecules, GenFish achieves higher mean performance across all three full-catalogue retrieval metrics than four discriminative neural baselines and three non-neural baselines, including ligand-based methods. A controlled positive-hiding experiment shows that targeted removal of false-negative competition improves contrastive retrieval beyond matched random masking; GenFish remains stronger under the same incomplete supervision. Pfam alignment indicates that the semantic identifiers capture biologically meaningful relationships among proteins. These results establish generative retrieval as a principled paradigm for reverse target fishing under incomplete bioactivity records.