PFArena is a new benchmark for evaluating language models in protein modification tasks, featuring four controlled interfaces that span single‑mutant generation and multi‑mutant ranking. It incorporates varying levels of mutation fitness data to represent four research scenarios with different amounts of prior experimental context. The benchmark tests six protein language models, six large language models, and five LLM‑based agents, finding that PLMs excel at open‑ended single‑mutant generation while LLMs and agents perform best in multi‑mutant ranking when target‑specific data are available, yet all struggle as search space and mutation depth grow.
By Yawen Ouyang, Xinbo Zhang, Ziyuan Ma, Yixin Wu, Wenbin Liao, Feiran Zhang, Wenjie Li, Lihao Wang, Hao Wang, Xiaoqing Zheng, Xuefeng Yan, Lei Bai, Ya-Qin Zhang, Shuyi Zhang, Wei-Ying Ma, Dahua Lin, Bowen Zhou, Hao Zhou
arXiv:2606. 18961v1 Announce Type: new Abstract: Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets.
By Lanqing Li, Shentong Mo, Yang Yu, Pheng-Ann Heng
Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets. To overcome this supervision bottleneck, we introduce unsupervised reward optimization of PLMs, a comprehensive framework for steerable protein generation without ground-truth labels.
arXiv:2505. 20346v3 Announce Type: replace-cross Abstract: Function-guided protein design is a crucial task with significant applications in drug discovery and enzyme engineering.
By Jiahao Kuang, Nuowei Liu, Jie Wang, Changzhi Sun, Tao Ji, Yuanbin Wu
arXiv:2609.05818v1 Announce Type: new
Abstract: We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use...
By Bryce Cai, Geetha Jeyapragasan, Samira Nedungadi, Jake Yukich, Seth Donoughe
arXiv:2608. 15669v1 Announce Type: new Abstract: Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs.
By Zhongwei Yu, Yan Song, Xue Yan, Anjie Liu, Xingyu Lu, Yihang Chen, Huichi Zhou, Siyuan Guo, Luoyang Sun, Sihan Chen, Xiangning Yu, Jun Wang
The paper introduces a flexible sampling technique for masked language models (MLMs) called stochastic beam search, which leverages MLMs’ efficiency in evaluating the pseudo‑perplexity of a sequence’s 1‑edit neighborhood. This method reframes generation as whole‑sequence evaluation, allowing guidance across multiple optimization objectives. Extensive in‑silico and in‑vitro tests on antibody therapeutics demonstrate that the sampling strategy significantly influences outcomes, highlighting the need for further research in this area.
By Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober, Hunter Elliott
arXiv:2606. 02624v1 Announce Type: cross Abstract: AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather than merely fit static measurements.
By Jin Gao, Juntu Zhao, Zirui Zeng, Jiaqi Shen, Junhao Shi, Dukun Zhao, Yuming Lu, Dequan Wang
arXiv:2606. 08100v1 Announce Type: new Abstract: Multimodal $\Delta\Delta G$ predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations.
By A Shivram, Aneesh S. Chivukula, Manik Gupta, Sourav Chowdhury
arXiv:2606. 02386v1 Announce Type: new Abstract: Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical feedback or redirect generation when a candidate violates thermodynamic or structural constraints.
By Sahil Rahman, Maxx Richard Rahman
arXiv:2604.07669v3 Announce Type: replace-cross
Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...
By Tao Li, Kaiyuan Hou, Tuan Vinh, Fanglei Xue, Monika Raj, Zhichun Guo, Carl Yang
arXiv:2605. 00182v3 Announce Type: replace Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints.
By Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng, Yu Li, Shujian Huang, Quanquan Gu