Claude Skills by InternScience

'ATC Drug Classification Lookup - Look up drug in ATC classification:

Predict protein-ligand binding affinity using Boltz-2 model to assess

'Cancer Therapy Design - Design cancer therapy: identify targets, find

'Chemical Safety Assessment - Assess chemical safety: PubChem compound

'Clinical Pharmacology Report - Generate clinical pharmacology report:

'Clinical Trial Drug Profiling - Profile drug for clinical trials: FDA

'Combinatorial Chemistry Library Design - Design combinatorial library:

'Comparative Drug Analysis - Compare drugs: structure analysis, PubChem

'Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline:

Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand

'Disease-Specific Compound Screening - Screen compounds for disease:

'Disease-Drug Landscape Analysis - Map the drug landscape for a disease:

'Disease Knowledge Graph - Build disease knowledge graph: OpenTargets

Comprehensive drug screening pipeline from molecular filtering through

'Drug-Indication Mapping - Map drug indications: ChEMBL drug indications,

Drug-Drug Interaction Checker - Check interactions between multiple drugs

'Drug Metabolism Study - Study drug metabolism: FDA metabolism data,

Drug Repurposing Screening - Screen existing drugs for new indications

Comprehensive Drug Safety Profile - Build a complete drug safety profile

Drug Target Identification Pipeline - Identify drug targets for a disease

'Drug-Target Structural Biology - Integrate drug and target structure:

'Drug Warning Intelligence Report - Generate drug warning report: ChEMBL

Predict the ADMET (absorption, distribution, metabolism, excretion, and

Retrieve SMILES strings from PubChem using compound names.

Check if the input protein sequence or molecule SMILES string is valid.

Generate new molecules de novo.

Calculate disease reversal scores for the provided molecules relative

Compute the drug-likeness metrics (QED score and Number of violations

Use ESMFold model to predict 3D structure of the input protein sequence.

Implement data transmission between the local computer and the MCP Server

Generate new molecules sampling from the input two warhead fragments.

Calculate different types of molecular properties based on SMILES strings,

Compute the Tanimoto similarities between a target molecule and a list

Generate new molecules sampling from the input molecule.

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Generate new peptide molecules sampling from the input peptide sequence.

Given a protein sequence and its structure, employ ProSST model to predict

Generate new molecules sampling from the input scaffold.

Search the protein information from the input gene name and downloads

'Enzyme Inhibitor Design - Design enzyme inhibitor: target structure,

'Epigenetics & Drug Response - Link epigenetics to drug response: gene

Assess drug risks and adverse effects using FDA drug database to retrieve

Given a gene symbol (e.g. TPMT), query 3 public databases (ClinGen CAR,

'Gene-to-Drug Discovery Pipeline - Full gene-to-drug pipeline: gene lookup,

'Gene-Variant-Drug Nexus - Connect gene variants to drugs: variant effect,

'Infectious Disease Analysis - Analyze infectious disease: virus data,

'Lead Compound Optimization - Optimize a lead compound: validate SMILES,

'Molecular Docking Pipeline - Complete docking workflow: retrieve protein

'One Health Pathogen Analysis - One Health analysis: pathogen genome,

Retrieve disease-associated targets from Open Targets using disease EFO