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Analyze protein structural changes using molecular dynamics (MD) simulation.
Predict highly accurate protein 3D structures from amino acid sequences using deep learning.
Generate new protein backbone structures or perform motif-based design based on deep learning.
Design optimal sequences based on protein 3D structures.
Rapidly predict solubility from protein sequence.
Predict thermal stability (Tm) based on protein sequence.
Predict the toxicity potential of protein or peptide sequences.
Supports ADMET properties and toxicity prediction of compounds.
Predict catalytic activity (kcat) from enzyme sequence.
Predict receptor-ligand complex structures using diffusion models.
Quantitatively predict binding affinity (ΔG) of protein-protein complexes.
Design protein and biomolecule complexes with structural and interaction constraints using protein, compound, and CCD combinations.
Predict protein-compound interactions by fusing molecular and protein features through the attention mechanism.
Design optimal protein sequences considering thermal stability.
Design optimal sequences for ligand-binding proteins.
Simultaneously optimize antibody sequence and 3D structure.
Explore and generate ligand pockets from protein structures.
Automatically analyze protein-ligand complex interactions.
Improve quality by correcting incorrect parts of protein structures.
Infer gene regulatory networks from gene expression data using ensemble tree-based methods.
Model 3D structures of antibody-antigen complexes.
Predict and visualize functions (GO terms) based on protein sequences and structures.
Supports compound property calculations, similarity evaluation, and structural transformation.
Analyze and visualize single-cell RNA sequencing data using scalable Python tools.
Create or evaluate new compounds using deep learning.
Simulate complex biological systems using a large-scale biomedical language model.
AI predicts interaction potential from protein sequence pairs.
Protein-Protein Binding Affinity Prediction Tool