
What Transfers Across Protein Pockets? Experience Reuse in Molecular Optimization for Agentic Structure-Based Drug Design
This paper studies experience reuse for LLM agents that optimize ligands in protein pockets by editing and docking. Using the full log of one agent run over CrossDocked2020 test-set pockets, it tests whether keys in an experience base recur on unseen pockets and separate improving from worsening edits. The separation is largely explained by molecule size change, action and round, not the target. The authors conclude a base may better prioritize an agent's own proposals for docking than prescribe what to build.
What it claims
The paper tests whether docking measurements collected on one protein pocket stay useful when reused on a pocket never seen before. It builds an experience base from a single full run of the PROBE agent over the CrossDocked2020 test-set pockets, yielding 14,341 records (5,398 from the probe phase, 8,943 from design-make-test-analyze edits). The finding: the keys that still separate good edits from bad ones on a held-out pocket mostly describe the edit and the molecule it produced, not the target. No evaluated key satisfies all three requirements at once: use pocket information, recur on an unseen pocket, and still separate outcomes.
How it works
Each record stores the pocket, ligand, round, editing action, four fragment constraints, pocket-ligand contact features, and the outcome (ΔA affinity change, ΔN heavy-atom change). Affinity is A(m) = −Vina(m). PROBE optimizes 10 MolCRAFT-generated ligands per pocket through a probe phase (round 0) and five DMTA rounds (1–5). A "key" is a tuple of record properties; the base pools records matching a candidate edit and predicts improve when improving records outnumber worsening ones. Every held-out test splits at the pocket, so a record is always labeled by a base that never saw its pocket. Separation is measured two ways: a Cochran-Mantel-Haenszel stratified risk difference, and a bias-corrected ω² on outcome residuals after fitting out ΔN and round.
Evidence
The records' built-in key κ0 = (contact features, action, round) separates edits by +53.3pp. But holding the action, ΔN, and round equal for the two Replace actions shrinks the gap to +0.3pp [−5.1, +6.3], so the contact features (the only pocket field) add nothing detectable. In the cross-pocket table, ligand-change keys win: Δrings beats a ΔN baseline on 91% of pockets (+8.1pp lift), Δrotatable bonds on 85% (+8.0pp), Δaromatic atoms on 70% (+6.8pp), fragment constraints on 63% (+5.1pp). Pocket-description keys either fail to recur (all six descriptors: 17% recurrence) or fail to separate (burial, hydrophobic fraction: 100% recurrence, below baseline).
Limits
The authors recommend using the base to rank an agent's own proposals for docking, not to prescribe what to build; ranking gains and docking savings are not measured. Evidence is one pipeline (PROBE), one initial-ligand generator (MolCRAFT), one scorer (Vina, not experimental binding), one pocket-descriptor set, and one key family: categorical exact-match lookups. Similarity-based pooling and combining pocket descriptors with ligand-change keys are untested. Intervals are unadjusted for multiple comparisons.
Organisations named here
- arXiv
Technologies named here
- CrossDocked2020
- LLM
Techniques named here
- Agentic structure-based ligand optimization
- Experience base with keys
- Key transfer analysis on unseen pockets
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