Skill UI

Biopython provides Python APIs for sequence manipulation, database access, BLAST automation, structural bioinformatics, and phylogenetics, making it ideal for scripting custom pipelines and batch processing in computational molecular biology workflows.

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

Molfeat is a Python toolkit that converts SMILES/RDKit molecules into embeddings via 100+ calculators, transformers, and pretrained models for QSAR, screening, similarity search, and deep learning pipelines.

Access RCSB PDB to search biomolecular structures via text, sequence, or geometry queries, download coordinate files in PDB/mmCIF/Binary formats, retrieve metadata, and batch-process entries for drug discovery, protein engineering, and structural biology pipelines.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

RDKit delivers a Python-based cheminformatics toolkit for parsing SMILES/SDF, computing descriptors, generating 2D/3D coordinates, fingerprinting, searching substructures, managing reactions, and visualizing molecules, ideal for drug discovery and computational chemistry workflows.

Cloud-native Rowan orchestrates medicinal chemistry and molecular modeling pipelines via a Python API, offering pKa prediction, docking, descriptors, dynamics, and AI-driven structure workflows for scalable batch screening without local HPC.

Run OpenMM-powered molecular dynamics simulations and MDAnalysis trajectory workflows to build protein or small-molecule systems, minimize energy, equilibrate conditions, and characterize RMSD/RMSF, contacts, and free-energy surfaces for structural biology or drug studies.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

Builds molecular property prediction and MoleculeNet workflows with DeepChem: SMILES and graph featurization, scaffold or grouped holdouts, masked labels, graph models, and pretrained encoder transfer for ADMET, toxicity and solubility ML.

Process, clean, compare, and search tandem mass spectra with matchms 0.33.1. Covers MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity scoring, library matching, score matrices, and molecular-similarity networks in Python.

Python library for medicinal chemistry filtering of compound libraries. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alerts (PAINS, NIBR, ChEMBL), complexity metrics, chemical group detection, and a query language for large-scale molecular triage in drug discovery.