Bridging technical knowledge gaps in computational biophysics

Computational biophysics is a field that encompasses multiple domains of knowledge, not just in theoretical understanding but in the deployment of computational methods as well, posing research barriers for beginners and experts alike.

For example, to design an antibody, researchers must be proficient in artificial intelligence, biophysics, and molecular dynamics to successfully execute structural prediction, molecular docking, and binding interaction validations. The tools used in each of these steps require in-depth experience to be used correctly.

Xia et al. note such challenges in elucidating complex biological systems.

“Computational biophysics encounters barriers lying in the specialized knowledge and the practical usage of computational methods, the fragmented computational hardware and software ecosystems, and the unstructured and multimodal biophysical data, which create steep learning curves for building efficient workflows, even for experienced researchers,” said author Yi Qin Gao.

As a result, large language models (LLMs) are gathering interest in computational biophysics for their scalability and domain-specific task handling. The researchers extend this approach in their development of Agent for Digital Atoms and Molecules (ADAM).

“ADAM is a multi-agent framework that employs a divide-and-conquer strategy to decompose complex biophysical problems into subtasks handled by specialized subagents,” said Gao. “It enables asynchronous, database-centric tool orchestration, fostering community-driven extensibility and collective innovation.”

In essence, ADAM works as a team of LLM agents, applying scientific tools for each research step in response to user input, so there is no need for the user to understand the specifics of the technical content.

Building on this work, the authors plan to implement long-term memory functions to support user-specific learning, producing personalized outputs that fully assist in professional development.

Source: “Large language models as AI agents for digital atoms and molecules: Catalyzing a new era in computational biophysics,” by Yijie Xia, Xiaohan Lin, Zicheng Ma, Jinyuan Hu, Yanheng Li, Zhaoxin Xie, Hao Li, Li Yang, Zhiqiang Zhao, Lijiang Yang, Zhenyu Chen, and Yi Qin Gao, APL Computational Physics (2025). The article can be accessed at https://doi.org/10.1063/5.0283692 .