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Found 7 papers, 5 papers with code
SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics.
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning, thereby expediting progress in the field of artificial intelligence-driven drug discovery.
Multi-level Interaction Modeling for Protein Mutational Effect Prediction
Protein-protein interactions are central mediators in many biological processes.
Full-Atom Peptide Design with Geometric Latent Diffusion
Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable.
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.
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.
