by K-Dense-AI
torchdrug is a PyTorch-native toolkit for molecular and protein machine learning. Use the torchdrug skill to choose tasks, datasets, and modular models for graph neural networks, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis. It is best for custom model development and reproducible configs, not just canned demos.
by K-Dense-AI
The rdkit skill helps with precise cheminformatics workflows: parsing SMILES, SDF, MOL, PDB, and InChI; calculating descriptors; generating fingerprints; running substructure search; handling reactions; and building 2D/3D coordinates. Use this rdkit guide for advanced control, custom sanitization, and rdkit for Data Analysis workflows.
by K-Dense-AI
Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. The rowan skill is best for batch pKa prediction, conformer and tautomer ensembles, docking, cofolding, molecular dynamics, permeability, and descriptor workflows when you want reproducible, programmatic runs without managing local HPC or GPU infrastructure.
by K-Dense-AI
pytdc is a skill for Therapeutics Data Commons, giving AI-ready drug discovery datasets and benchmarks for ADME, toxicity, DTI, DDI, generation, scaffold splits, and pharmacological prediction.
by K-Dense-AI
primekg is a PrimeKG knowledge-graph skill for academic research, linking genes, drugs, diseases, phenotypes, and paths for evidence-oriented biomedical exploration and drug repurposing.
by K-Dense-AI
The molecular-dynamics skill helps you set up, run, and analyze molecular dynamics simulations with OpenMM and MDAnalysis for Scientific workflows. Use it for protein stability, ligand binding, conformational sampling, and trajectory analysis such as RMSD, RMSF, contact maps, and free energy surfaces. It focuses on practical setup, force fields, and reproducible execution.
by K-Dense-AI
medchem is a medicinal chemistry filtering skill for Scientific workflows. Use it to apply Lipinski, Veber, PAINS, structural alerts, and complexity metrics for compound prioritization, library cleanup, lead optimization, and compound quality review.
