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Found 6 papers, 3 papers with code
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Our key innovation is to develop a multi-stage diffusion process.
Protein-ligand binding representation learning from fine-grained interactions
The binding between proteins and ligands plays a crucial role in the realm of drug discovery.
UniMAP: Universal SMILES-Graph Representation Learning
Molecular representation learning is fundamental for many drug related applications.
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design.
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery.
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery.
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