arXiv:2601.22466v2 Announce Type: replace Abstract: Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the underlying statistical manifolds. We address this issue by representing molecules using composite exponential-family distributions, where coordinates and categories are represented within a unified natural parameter space to evolve synchronously along exponential geodesics und
The paper addresses a fundamental limitation in existing computational drug design by introducing a more integrated and statistically sound method for molecule representation and evolution.
This scientific advancement has the potential to significantly accelerate and enhance the efficiency of drug discovery pipelines, impacting pharmaceutical R&D costs and timelines.
Drug discovery methodologies could shift towards more sophisticated, unified computational models, potentially reducing the need for extensive trial-and-error in early-stage design.
- · Pharmaceutical R&D
- · Biotech companies
- · AI in drug discovery
- · Patients with unmet medical needs
- · Traditional high-throughput screening methods
- · Companies relying on less efficient legacy drug design platforms
More accurate and faster identification of potential drug candidates.
Reduced time and cost in preclinical drug development, leading to a higher success rate for new therapies.
A potential increase in the number of novel drugs brought to market, addressing a broader range of diseases more effectively.
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Read at arXiv cs.LG