Hugging Face Trending Papers
PFArena: Benchmarking Language Models for Protein Modification
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arXiv AI
Sep 25
PFArena: Benchmarking Language Models for Protein Modification
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.
arXiv Machine Learning
Jun 18
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
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.
Hugging Face Trending Papers
Jun 17
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
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 AI
Jul 7
PDFBench: A Benchmark for De novo Protein Design from Function
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.
arXiv AI
Sep 10
Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools
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...
arXiv Machine Learning
Aug 18
Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
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.