@inproceedings{wang-etal-2026-mdforge,
title = "{MDF}orge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback",
author = "Wang, Zehong and
Ma, Yijun and
Schmidt, Connor R. and
Ma, Tianyi and
Sun, Weixiang and
Li, Ziming and
Guo, Xiaoguang and
Zhang, Chuxu and
Webber, Matthew J. and
Ye, Yanfang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.672/",
pages = "15482--15507",
ISBN = "979-8-89176-459-0",
abstract = "Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principles physics. Designing an MD pipeline for a new system requires substantial expert knowledge: running it on even one molecule is expensive, ruling out trial-and-error. We automate this expert pipeline-design process with an LLM agent. Unlike existing MD agents that orchestrate a predefined tool set, we treat pipeline design as open-ended code generation in which the agent{'}s behavior is reshaped online by verbal reward. Specifically, we build MDForge, an LLM agent whose in-context update rule densifies the sparse reward via a multi-agent debate among physics experts. On three SAMPL host{--}guest binding free-energy benchmarks (CB[7], OAH, CBClip), MDForge automatically designs MD pipelines competitive with human experts. Deployed on a library of unseen candidate guests, its CB[7] pipeline discovers a novel binder that wet-lab competition NMR confirms is a high-affinity, picomolar CB[7] binder ($K_a \approx 8 \times 10^{12}${~}M$^{-1}$). Our data and code are available at https://github.com/Zehong-Wang/MDForge."
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<abstract>Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principles physics. Designing an MD pipeline for a new system requires substantial expert knowledge: running it on even one molecule is expensive, ruling out trial-and-error. We automate this expert pipeline-design process with an LLM agent. Unlike existing MD agents that orchestrate a predefined tool set, we treat pipeline design as open-ended code generation in which the agent’s behavior is reshaped online by verbal reward. Specifically, we build MDForge, an LLM agent whose in-context update rule densifies the sparse reward via a multi-agent debate among physics experts. On three SAMPL host–guest binding free-energy benchmarks (CB[7], OAH, CBClip), MDForge automatically designs MD pipelines competitive with human experts. Deployed on a library of unseen candidate guests, its CB[7] pipeline discovers a novel binder that wet-lab competition NMR confirms is a high-affinity, picomolar CB[7] binder (K_a \approx 8 \times 10¹2 M⁻1). Our data and code are available at https://github.com/Zehong-Wang/MDForge.</abstract>
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%0 Conference Proceedings
%T MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback
%A Wang, Zehong
%A Ma, Yijun
%A Schmidt, Connor R.
%A Ma, Tianyi
%A Sun, Weixiang
%A Li, Ziming
%A Guo, Xiaoguang
%A Zhang, Chuxu
%A Webber, Matthew J.
%A Ye, Yanfang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-mdforge
%X Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principles physics. Designing an MD pipeline for a new system requires substantial expert knowledge: running it on even one molecule is expensive, ruling out trial-and-error. We automate this expert pipeline-design process with an LLM agent. Unlike existing MD agents that orchestrate a predefined tool set, we treat pipeline design as open-ended code generation in which the agent’s behavior is reshaped online by verbal reward. Specifically, we build MDForge, an LLM agent whose in-context update rule densifies the sparse reward via a multi-agent debate among physics experts. On three SAMPL host–guest binding free-energy benchmarks (CB[7], OAH, CBClip), MDForge automatically designs MD pipelines competitive with human experts. Deployed on a library of unseen candidate guests, its CB[7] pipeline discovers a novel binder that wet-lab competition NMR confirms is a high-affinity, picomolar CB[7] binder (K_a \approx 8 \times 10¹2 M⁻1). Our data and code are available at https://github.com/Zehong-Wang/MDForge.
%U https://aclanthology.org/2026.emnlp-main.672/
%P 15482-15507
Markdown (Informal)
[MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback](https://aclanthology.org/2026.emnlp-main.672/) (Wang et al., EMNLP 2026)
ACL
- Zehong Wang, Yijun Ma, Connor R. Schmidt, Tianyi Ma, Weixiang Sun, Ziming Li, Xiaoguang Guo, Chuxu Zhang, Matthew J. Webber, and Yanfang Ye. 2026. MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15482–15507, Budapest, Hungary. Association for Computational Linguistics.