Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance
arXiv:2604.24474v2 Announce Type: replace Abstract: Molecular similarity plays a central role in ligand-based drug discovery, such as virtual screening, analog searching, and goal-directed molecular generation. However, traditional similarity measures, ranging from fingerprint-based Tanimoto coefficients to 3D shape overlays, are often computationally expensive at scale or rely on hand-crafted molecular descriptors. Meanwhile, many deep learning approaches to similarity-aware design still depend on similarity-specific supervision or costly data curation, limiting their generality across target
The proliferation of advanced AI techniques allows for more sophisticated and efficient approaches to molecular design, overcoming the limitations of traditional, computationally expensive methods.
This development significantly accelerates drug discovery and material science, enabling faster identification of new compounds and optimizing existing ones, directly impacting innovation in pharmaceuticals and biotechnology.
Drug discovery and material design processes can be significantly streamlined, moving away from laborious traditional methods towards highly efficient, AI-driven molecular generation and screening.
- · Pharmaceutical companies
- · Biotech firms
- · AI/ML drug discovery platforms
- · Material science researchers
- · Companies reliant on traditional, slow drug discovery methods
- · Manual molecular screening services
Reduced time and cost for drug development due to more efficient virtual screening.
An increase in the number of novel drug candidates entering clinical trials, potentially leading to more therapeutic breakthroughs.
The development of entirely new classes of materials with tailored properties, revolutionizing industries from aerospace to electronics.
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Read at arXiv cs.LG