arXiv AI August 24, 2026 By Gyubok Lee, Kiwoong Yoo, Jimin Seo, Kyunghoon Hur, Edward Choi

Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design

The study explores using large language models (LLMs) to create ranking policies for shortlisting protein binders after they have been generated by de novo design workflows. By combining precomputed structural‑confidence and interface‑quality proxy scores, the authors demonstrate that iterative LLM policies can modestly improve recall and NDCG metrics over single‑feature baselines. The approach offers an interpretable post‑generation decision layer that helps prioritize binders from large candidate pools.

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llms

arXiv Machine Learning

Aug 21

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

arXiv:2608. 19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening.

By Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang, Yew-Soon Ong, Yuangang Pan

rag diffusion benchmarks

arXiv AI

Jul 29

REPREC: Representation Driven Parameter-Efficient Recommendation System

arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.

By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra

llms rag fine-tuning efficiency benchmarks safety

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...

By Bryce Cai, Geetha Jeyapragasan, Samira Nedungadi, Jake Yukich, Seth Donoughe

llms agents benchmarks

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.

llms reinforcement-learning fine-tuning

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.

By Lanqing Li, Shentong Mo, Yang Yu, Pheng-Ann Heng

llms reinforcement-learning fine-tuning

Hugging Face Trending Papers

Aug 19

Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson

The paper presents a cost‑effective approach for industrial explainable‑recommendation systems by decoupling explanation generation from selection. Explanations are pre‑generated using six prompt styles and two commodity LLMs, then a lightweight CPU‑resident selector (e.g., LambdaRank) chooses the best one at request time, achieving sub‑100 ms latency without GPUs. Experiments on a 2,958‑pair Google Local subset and a 300‑pair MovieLens‑1M split show that pairwise ranking outperforms single‑action RL methods, while KG‑path selectors achieve near‑perfect unique‑output rates, and the overall end‑to‑end build cost is around $15 on commodity hardware.

llms reinforcement-learning efficiency benchmarks

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Related stories

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

REPREC: Representation Driven Parameter-Efficient Recommendation System

Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools

Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization

Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization

Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson