arXiv:2512.02328v2 Announce Type: replace-cross Abstract: Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes. MolAS is a lightweight algorithm-selection model that predicts per-algorithm performance from pretrained protein and ligand embeddings using attentional pooling and a shallow residual decoder. With hundreds to a few thousand labelled complexes, MolAS achieves up to a 15 percentage-point absolute improvement over the single-best solver (SBS) and closes 17--66\% of the Virtual Best
The proliferation of AI in drug discovery and molecular science necessitates more efficient and intelligent methods for algorithm selection, moving beyond manual trial-and-error.
This AI-driven approach to algorithm selection significantly improves the efficiency and accuracy of protein-ligand docking, a critical step in drug design and materials science, accelerating discovery and reducing costs.
Traditional reliance on single-best solver approaches or exhaustive testing is replaced by an AI model that predicts optimal algorithm performance, leading to more targeted and effective molecular simulations.
- · Pharmaceutical companies
- · Biotechnology sector
- · Materials science researchers
- · AI algorithm developers
- · Traditional drug discovery methods
- · Inefficient R&D pipelines
Faster and more cost-effective drug development cycles become possible due to improved molecular docking efficiency.
Reduced investment risk in early-stage drug candidates leads to a broader pipeline of potential therapies.
The acceleration of new drug and material discovery could lead to significant health and industrial advancements, impacting global economies over time.
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Read at arXiv cs.LG