Predict comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties for drug candidate molecules using GraphMVP ensemble models. Use this skill when: (1) Predicting blood-brain barrier penetration, (2) Assessing side effect profiles, (3) Estimating Caco-2 permeability, half-life, or LD50 toxicity, (4) Evaluating drug-likeness and safety of molecules.

Antibody design using IgGM model. Use this skill when: (1) Epitope-conditioned de novo antibody design, (2) Antibody affinity maturation, (3) Using antigen PDB structure and epitope information. For binding affinity evaluation, use prodigy.

Antibody-related structure prediction using tfold model. Use this skill when: (1) Predict antibody and nanobody structure of a given sequence, (2) Predict antigen-antibody complex structure of given sequences, (3) Using local GPU resources. For binding affinity evaluation, use prodigy.

Call accessible chromatin peaks from ATAC-seq BAM files, annotate peaks to genomic features and genes, and identify differentially accessible regions between experimental conditions.

Trim adapters, align reads, remove duplicates and mitochondrial contamination, and evaluate chromatin accessibility data quality before calling peaks.

Protein complex binding affinity prediction. Use this skill when: (1) Predict the binding affinity score, (2) Using protein complex structure.

A meta-skill for creating and improving skills in the OpenBioMed biomedical toolkit.

Search biomedical literature from PubMed and bioRxiv for research papers. Use this skill when: (1) Finding research papers on a specific topic or disease, (2) Retrieving recent preprints from bioRxiv, (3) Getting paper titles, abstracts, and metadata, (4) Literature review for drug discovery or biomedical research.

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets and metadata, (3) Training machine learning models on single-cell data, (4) Performing large-scale cross-dataset analyses.

Query ChEMBL database for bioactivity data on drug-like compounds. Use this skill when: (1) Finding compounds active against a protein target (target-based search), (2) Getting bioactivity profile for a molecule (molecule-based search), (3) Finding drugs for a disease indication (indication-based search).

Analyze potential drug-drug interactions (DDI) for up to 5 drugs using KEGG DDI database. Use this skill when: (1) Checking interactions between multiple medications, (2) Assessing DDI risk for drug combinations, (3) Understanding interaction mechanisms and severity, (4) Analyzing CYP enzyme involvement in DDIs.

Identify the IUPAC name of a molecule using BioT5 question answering model. Use this skill when: (1) User wants to find the IUPAC name of a molecule, (2) User asks "What is the IUPAC name?" or "What's the systematic name?", (3) User provides a SMILES string and wants the IUPAC nomenclature.

Query KEGG database for drug information, pathway analysis, and disease-drug-target discovery. Use this skill when: (1) Looking up drug information including efficacy, targets, metabolism, and interactions, (2) Analyzing metabolic or signaling pathways to retrieve genes, compounds, and modules, (3) Discovering disease-associated drugs, genes, and pathways for drug repurposing.

Query a molecule's biochemical significance and roles in biology and chemistry using BioT5 multi-modal model. Use this skill when: (1) Understanding a molecule's biological roles and functions, (2) Describing a molecule's chemical significance and applications, (3) Getting natural language explanations of molecular properties, (4) Summarizing what a molecule is used for or its metabolic relevance.

Prepare your RNA-seq, proteomics, methylation, and other omics datasets for joint integration by applying per-assay normalization, cross-assay batch correction, feature ID alignment, and missing value handling.

Propose high-fitness and high-diversity mutants of the VP1 capsid protein of Adeno-Associated Virus (AAV) through multi-round iterative optimization.

Propose high-fluorescence and high-diversity mutants of Green Fluorescent Protein (GFP) through multi-round iterative optimization.

Search MS2 spectra against a protein sequence database to identify peptides and proteins in your sample. Apply target-decoy FDR filtering to control false discovery rate at both PSM and protein levels.