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Search Gene-Drug , found 17 results

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ClinPGx Database Access

clinpgx-database

K-Dense-AI/claude-scientific-skills

Provide REST API access to ClinPGx, the successor to PharmGKB, for querying gene-drug interactions, CPIC/DPWG/FDA guidelines, allele functions, and evidence that supports precision dosing and pharmacogenomics-driven clinical decisions.

KEGG REST Toolkit

K-Dense-AI/claude-scientific-skills

Direct Python helpers for KEGG's REST API, covering database metadata, listing, search, retrieval, ID conversion, linkage, and drug interactions to power pathway and gene analysis.

Therapeutics Data Commons

K-Dense-AI/claude-scientific-skills

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.

Advanced Molecular Control

K-Dense-AI/claude-scientific-skills

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.

TorchDrug GNN Toolkit

K-Dense-AI/claude-scientific-skills

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

DepMap Dependency Explorer

K-Dense-AI/claude-scientific-skills

Access DepMap data to fetch CRISPR Chronos dependency scores, drug sensitivity readouts, and gene effect profiles for cancer cell lines, supporting target validation, biomarker discovery, and synthetic-lethality research via API or downloads.

PrimeKG Precision Explorer

K-Dense-AI/claude-scientific-skills

Query the PrimeKG precision medicine knowledge graph to explore genes, drugs, diseases, phenotypes, and their relationships, supporting drug discovery, repurposing, and phenotype analysis through programmatic scripts.

DepMap Cancer Dependency Analysis

K-Dense-AI/scientific-agent-skills

Retrieves and analyzes Cancer Dependency Map release data, including CRISPR Chronos gene effects, model annotations, omics biomarkers, and PRISM drug sensitivity for selective dependency and synthetic-lethality analyses.

PrimeKG Knowledge Graph Query

K-Dense-AI/scientific-agent-skills

Query the PrimeKG precision medicine knowledge graph to search genes, drugs, diseases, and phenotypes, retrieve associated neighbors, analyze disease context, and explore drug repurposing paths.

TorchDrug Graph Learning Workflow

K-Dense-AI/scientific-agent-skills

TorchDrug is a modular PyTorch library for graph learning tasks including molecular property prediction, molecule generation, and knowledge graph reasoning. It provides datasets, models, and training engines for building workflows in computational chemistry and biology. Ensure compatibility with PyTorch 1.8-2.0 and Python 3.7-3.10 before implementation.

Extracting Clinical Biomedical Entities

extracting-clinical-entities

maziyarpanahi/openmed

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.

Mining PubMed Literature with NCBI E-utilities

mining-pubmed-literature

maziyarpanahi/openmed

This skill programmatically searches and fetches biomedical literature from PubMed and PMC using NCBI E-utilities (ESearch, EFetch, ESummary). It allows users to gather large corpora of abstracts, full texts, and metadata related to specific conditions, drugs, or genes. Ideal for building evidence bases, performing biomedical Named Entity Recognition (NER), or summarizing scientific findings.

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