arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
By Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang, Kerstin Klaeser, Cristian Gabellini, Nikhil Shenoy, Francesco Di Giovanni, Prudencio Tossou
arXiv:2608. 02642v1 Announce Type: cross Abstract: Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort.
By Nithishwer Mouroug Anand, Wei-Tse Hsu, Kyle Vaccaro, Eden James Gage, Jonathan David Colburn, Linda Xi Phan, Minjoon Seo, Kevin Guan, Philip C. Biggin
R-GroundBench is a new diagnostic benchmark for evaluating AI models on R‑group grounding in Markush molecular editing, derived from real pharmaceutical patents. It includes a Multiple‑Choice VQA track with varying difficulty and modality splits, as well as an open‑ended Generation track. Experiments show a large performance gap: models score over 90% on easy VQA but drop to 56–66% on hard VQA, and generation exact match stays below 20% (and under 8% with visual input).
By Xin Wang, Zichuan Ying, Xinna Lin, Junqi Zhang, Hanyi Xiong, Tianyu Gao, Hairong Zhang, Qixiang Hua, Botian Shi, Zhenhailong Wang, Kaicheng Yu
MolDesignBench is a new benchmark for evaluating large language model (LLM)-based agents in scenario‑grounded molecular design. It contains 2,000 generation and optimization tasks that blend implicit narrative requirements with explicit property and functional‑group constraints, including infeasible cases, and require the use of 17 specialized chemistry tools. Experiments with leading LLMs show low success rates (best ~43%) and highlight failures in implicit‑constraint reasoning, infeasibility detection, and tool usage, underscoring the benchmark’s role in identifying key bottlenecks for future research.
By Yongjun Jeong, Hanbum Ko, Ye Rin Kim, Chanhui Lee, Rodrigo Hormazabal, Jaewan Lee, Sehui Han, Sungbin Lim, Sungwoong Kim
arXiv:2606. 09520v1 Announce Type: cross Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist?
By Junyi Gong, Zijie Qiu, Ben Zhong Tang
The paper introduces LibStructBench, a document‑grounded benchmark designed to assess whether AI agents can accurately reconstruct sequencing‑library structures and molecular workflows from protocols. Experiments reveal that current state‑of‑the‑art agents often generate roughly correct reconstructions but struggle with exact molecular‑state representation and workflow connectivity. The study also finds that changing the underlying language model yields larger performance differences than changing agent harnesses, and that additional knowledge only modestly improves oligo‑family recovery without affecting overall workflow accuracy.
By Chi-Lam Poon, Michael Goneos, Zezheng Song, Xi Chen