arXiv:2602.13249v2 Announce Type: replace-cross Abstract: Small-molecule foundation models are typically pretrained on standalone molecular data, unlike vision and language models that often benefit from cross-modal or relational supervision. Protein-ligand co-folding provides a molecular analogue of such supervision by exposing models to atom-level ligand-protein interactions, raising the question of whether co-folding models can yield strong small-molecule representations. We study this question using Boltz2, a modern co-folding model, by transferring its atom-level ligand representations to
The paper leverages a new co-folding model (Boltz2) to evaluate small-molecule representations, indicating a maturing of computational methods for drug discovery and molecular design.
This research could significantly improve the efficiency and efficacy of small-molecule drug discovery and materials science by providing more powerful AI models for molecular representation.
The ability to generate stronger small-molecule representations through co-folding models changes how AI can be applied to understanding and designing molecular interactions, potentially accelerating innovation in therapeutics and materials.
- · Pharmaceutical companies
- · Biotechnology firms
- · AI model developers
- · Drug discovery platforms
- · Traditional small-molecule screening methods
- · Companies reliant on less efficient R&D processes
Improved lead compound identification and optimization in drug discovery.
Reduced R&D costs and accelerated time-to-market for new drugs and materials.
The development of entirely new classes of therapeutics or materials previously impossible to design computationally.
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Read at arXiv cs.LG