Chat-Driven Computational (Bio)chemistry: Using LLM Agents to Accelerate Bio- and Chemoinformatics

Large-language models (LLMs) have rapidly become essential in software engineering, evolving from simple code suggestion tools to autonomous agents that directly read, modify, compile, and test local code bases. Recent LLMs perform well in software engineering benchmarks, showing good performance on complex multifile projects, generating new options for improving and developing bio- and chemoinformatic tools. We showcase this capability with the AMBER molecular dynamics suite, where the setup program LEaP suffered an O(N²) merge routine and a 32-bit integer overflow, limiting simulation systems to ∼6 million atoms. By using an LLM, we implemented an optimized unit merge algorithm and 64-bit indexing, cutting the parametrization time by more than 10-fold for mid-sized systems and allowing one to parametrize multimillion-molecule systems. This case illustrates how natural scientists can make use of LLM agents to modernize, optimize, and develop computational (bio)chemistry tools while also raising new challenges for software provenance and developer roles.