arXiv:2606.11508v1 Announce Type: new Abstract: Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging because ADME endpoints are noisy, interdependent, and often data-limited. We propose a molecular graph-transformer pretraining framework that combines chemistry-specific self-supervision with contrastive mutual information machine learning (cMIM). Our method encodes molecular graphs into latent variables, reconstructs SMILES strings from the graph-derived latent codes, and augments the contrastive obj
Advances in molecular graph-transformer models and contrastive learning are enabling more sophisticated approaches to drug discovery, making this development timely.
Improved ADME prediction accelerates drug discovery, reduces R&D costs, and enhances the safety and efficacy of new therapeutics, impacting pharmaceutical and healthcare sectors.
The ability to more accurately predict drug properties early in the development pipeline changes how pharmaceutical companies will design and screen candidate molecules.
- · Pharmaceutical R&D departments
- · Biotech startups
- · AI/ML drug discovery platforms
- · Patients
- · Traditional high-throughput screening methods
- · Companies reliant on older, less efficient drug discovery processes
Faster and cheaper development of new drugs, particularly for complex diseases.
Increased competition in drug development and a potential shift towards novel molecular entities with optimized properties.
The development of 'AI-designed' drugs becoming a standard industry practice, leading to a paradigm shift in drug discovery pipelines globally.
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Read at arXiv cs.LG