{"ID":2833768,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02328","arxiv_id":"2512.02328","title":"Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking","abstract":"Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, or protocol regimes. We introduce MolAS, a lightweight algorithm selection system that predicts per-algorithm performance from pretrained protein-ligand embeddings using attentional pooling and a shallow residual decoder. With only hundreds to a few thousand labelled complexes, MolAS achieves up to 15% absolute improvement over the single-best solver (SBS) and closes 17-66% of the Virtual Best Solver (VBS)-SBS gap across five diverse docking benchmarks. Analyses of reliability, embedding geometry, and solver-selection patterns show that MolAS succeeds when the oracle landscape exhibits low entropy and separable solver behaviour, but collapses under protocol-induced hierarchy shifts. These findings indicate that the main barrier to robust docking AS is not representational capacity but instability in solver rankings across pose-generation regimes, positioning MolAS as both a practical in-domain selector and a diagnostic tool for assessing when AS is feasible.","short_abstract":"Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, or protocol regimes. We introduce MolAS, a lightweight algorithm selection system that predicts per-algorithm performance from pretrained protein-ligand embeddings using attentional...","url_abs":"https://arxiv.org/abs/2512.02328","url_pdf":"https://arxiv.org/pdf/2512.02328v1","authors":"[\"Jiabao Brad Wang\",\"Siyuan Cao\",\"Hongxuan Wu\",\"Yiliang Yuan\",\"Mustafa Misir\"]","published":"2025-12-02T01:49:17Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}