Magnolia: A Persistent-Memory Agent Framework for Computational Chemistry

Authors: Tao Jiang , Benjamin Navet , Yousouf Taghzouti , Fabien Gandon , Louis-Félix Nothias , Élise Dumont

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Abstract: Chemistry has recently entered a high-data regime as molecular databases from high-throughput platforms or computational studies have become available. Increasing automation within the chemical sciences leads to a more digital and precision chemistry. Dedicated scientific agents are becoming central to guiding the chemist through an ever-increasing amount of data and findings, including negative ones. Here, we introduce Magnolia, a persistent-memory artificial intelligence agent framework that integrates computational protocols, from docking to quantum chemistry, lab notebooks and chat logs relying on large language models (LLMs). It relies on a three-tier memory system: session-level observations are automatically captured and distilled into structured learnings, confirmed and elevated to project-tier entries and, when human-validated, promoted to cross-project rules that actively guide subsequent work and provide direct chemical feedback. We illustrate Magnolia on four case studies: quantum-chemical evaluation of aromatic systems (COMPAS database), molecular dynamics of butane and of modified DNA, and a protein-peptide docking campaign. Across these projects, Magnolia tracked 317 runs and accumulated 116 project-tier entries, from which four cross-project rules were promoted; in controlled replays of four tasks, runs with memory passed the checks that runs without it failed (for example, ten of ten docking-configuration checks, against one to four of ten). By combining automation, reproducibility, and human-in-the-loop control, our Magnolia framework streamlines docking, molecular dynamics and quantum chemistry calculations with direct analysis and LLM-augmented feedback, offering accelerated access to computational (bio)chemistry.

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Subjects:

Artificial Intelligence (cs.AI) ; Biomolecules (q-bio.BM)

MSC classes:

68T01, 93A16

ACM classes:

I.2.11; I.2.6; I.2.8

Cite as:

arXiv:2610.27230 [cs.AI]

(or

arXiv:2610.27230v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.27230

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Louis-Felix Nothias [ view email ]

[v1]

Mon, 19 Oct 2026 19:59:12 UTC (4,701 KB)

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