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TOPIC · ENTITY #5638

docking

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RESEARCH · 1 SOURCE · arXiv cs.AI

Magnolia: persistent-memory agent framework for computational chemistry

Magnolia is a persistent-memory AI agent framework that integrates LLM-driven feedback with computational chemistry pipelines (docking, molecular dynamics, quantum chemistry), lab notebooks and chat logs via a three-tier memory (session → project → cross-project). The authors demonstrate Magnolia across four case studies (COMPAS aromatic evaluation, butane and modified-DNA MD, protein–peptide docking), reporting 317 tracked runs, 116 project-tier entries, four promoted cross-project rules, and improved task pass rates in controlled replays versus runs without memory.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

SpecOpt: contact-diff agentic method to optimize binding specificity of existing small molecules (arXiv:2609.21165v1)

This arXiv preprint defines 'specificity optimization' (SpecOpt): making constrained structural changes to existing drugs to increase binding preference for an intended target over measured off-targets while keeping drug-like properties. The authors provide a ChEMBL-derived benchmark and an agentic pipeline that docks compounds to targets and off-targets, encodes residue-aware atom–protein contact differences, asks a large language model to propose modifications, and filters candidates by similarity, ADMET, and docking selectivity; on 915 compounds the mean target–off-target binding gap shifted from −0.72 to +0.47 kcal/mol and 84.8% of compounds improved while mean Tanimoto similarity remained 0.72; ablations show residue-specific contact identities are critical.

7.0