Balto simplifies drug discovery by making information and molecular modeling more accessible:
- AI-Guided Conversations: Perform complex molecular modeling tasks through natural language interactions
- Molecular Data Retrieval: Accessing ChEMBL, PDB, AlphaFold, BindingDB, PubChem, and UniProt for compound and protein data.
- Pocket Identification: Detecting binding pockets on proteins with AI-powered PocketFinder.
- Molecular Docking: Performing ligand-protein docking simulations and analyzing results.
- ADMET & Molecular Property Predictions: Computing lipophilicity (LogP), distribution (LogD), solubility (LogS), cardiotoxicity (hERG), CYP binding, Ames mutagenicity, quantitative estimate of drug-likeness (QED), and synthetic accessibility score (SAS).
- Visualization Tools: Analyze protein and docked pose with 3D visualizations, assess protein-ligand interactions and molecular structures with 2D visualizations
- Structural analysis: Assess pose accuracy with RMSD calculation, process mutations with mutagenesis analysis
- Batch Processing: Processing up to 1,000 molecules per job for property predictions and docking.
- Exportable Results: Supporting PDB, CSV and SDF file exports for downstream analysis.
For a full list of functionalities, please see our documentation. You can also ask Balto directly about its capabilities.