Abstract:Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approaches for molecular generation often suffer from high rates of invalid and low-quality ligand candidates, a result of the syntactic limitations of current models with regard to molecular strings. In this paper, we introduce $\texttt{ToolMol}$, an evolutionary agentic framework for de novo drug design. $\texttt{ToolMol}$ combines a multi-objective genetic algorithm with an agentic LLM operator that iteratively updates the ligand population. We build a comprehensive toolbox of RDKit-backed functions that allows our agentic operator to consisently make precise ligand modifications. $\texttt{ToolMol}$ achieves state-of-the-art performance on multi-objective property optimization tasks, discovering drug-like and synthesizable ligands that have $>10\%$ stronger predicted binding affinity compared to existing methods, evaluated on three protein targets. $\texttt{ToolMol}$ ligands additionally achieve state-of-the-art results in gold-standard Absolute Binding Free Energy scores, gaining over existing methods by over $35\%$. By studying chain-of-thought reasoning traces, we observe that tool-calling enables the model to more faithfully execute its planned modifications, efficiently exploiting the strong chemical prior knowledge in LLMs.
| Comments: | 9 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2605.12784 [cs.LG] |
| (or arXiv:2605.12784v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12784 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Andrew Zhou [view email]
[v1]
Tue, 12 May 2026 21:58:14 UTC (3,154 KB)