arXiv:2507.13762v4 Announce Type: replace Abstract: Motivation: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates and discrete atom types remains a challenge. Current approaches, such as diffusion and flow matching models, often fail to unify these heterogeneous modalities, relying on separate strategies or ill-fitting Euclidean metrics for discrete variables. This lack of a consistent framework limits generative models' ability to capture the geometric and chemical structure of protein-ligand complexes. Resu
The continuous advancements in deep generative models are driving innovation in structure-based drug design, pushing for more integrated solutions to complex molecular generation problems.
Improving molecule generation capabilities has direct implications for the speed and efficiency of drug discovery, potentially accelerating the development of new therapeutics.
This model offers a unified approach to handle both continuous atomic coordinates and discrete atom types, overcoming a significant limitation in current generative models for protein-ligand interactions.
- · Pharmaceutical R&D organizations
- · AI Drug Discovery platforms
- · Biotech startups
- · Traditional drug discovery methods
- · Generative models with separate discrete/continuous strategies
More efficient and accurate design of novel drug candidates and materials.
Reduced timelines and costs in early-stage drug development, leading to faster market entry for new therapies.
A paradigm shift in how molecular compounds are designed, potentially unlocking new classes of drugs or materials with unprecedented properties.
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Read at arXiv cs.LG