Results for “molecular-docking”

Predict 3D binding poses of small molecule ligands to protein targets using diffusion-based molecular docking, supporting single complexes, batch processing, and virtual screening.

Applies physical principles to model biological systems, including protein folding, membrane transport, molecular forces, and neural signaling.

Discovers and uses scientific datasets, models, blog posts, and interactive demos from a curated catalog for AI/ML work in domains like biology, chemistry, physics, and genomics.

Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).

Run computational biology tools for protein structure prediction, design, docking, and molecular dynamics on managed cloud GPUs via REST API or MCP server.

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

Produces a decision-grade dossier on a specific company, person, nonprofit, or government org by testing the user's hypothesis rather than confirming it. Outputs an editable Word document with verdict, identity facts, 12-month activity timeline, network and reputation signals, and red flags.