105 件 ・ 関連度順
概要と使いどころ
Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.
日本語の概要は準備中です。原文の説明を表示しています。
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
日本語の概要は準備中です。原文の説明を表示しています。
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). ...
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete FAERS pharmacovigilance study designs for one-drug whole-profile safety mapping using signal detection, subgroup analysis, onset/seriousness characterization, and conservative label-gap interpretation.
日本語の概要は準備中です。原文の説明を表示しています。
Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
日本語の概要は準備中です。原文の説明を表示しています。
Use when evaluating stocks through Dan Bin's long-term compounder lens: buy great companies, hold through volatility, and let time do the work. Focus on consumer, pharma, and tech compounders.
日本語の概要は準備中です。原文の説明を表示しています。
Design Thinking method coaching knowledge across all nine methods including per-method techniques, deep expertise, and industry context (energy, financial services, healthcare, manufacturing, nonprofit and social impact, pharmaceuticals and life sciences, professional services, public sector, retail and CPG)
日本語の概要は準備中です。原文の説明を表示しています。
Routes finance: Bigdata/RavenPack, US fundamentals/yfinance, A-share news/policy, sector Top N/公告, Gangtise/岗底斯 setup. Reads one bundled specialist. Pharma reporting and general argument review keep their own direct entries.
日本語の概要は準備中です。原文の説明を表示しています。
Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.
日本語の概要は準備中です。原文の説明を表示しています。
Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.
日本語の概要は準備中です。原文の説明を表示しています。
Query CPIC pharmacogenomics clinical annotations database
日本語の概要は準備中です。原文の説明を表示しています。
The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Query the ClinPGx (formerly PharmGKB) REST API plus the CPIC PostgREST companion API for pharmacogenomic clinical annotations, CPIC/DPWG dosing guidelines, gene-drug pairs, variant-drug associations, FDA/EMA drug labels, and PGx pathways. Two-host architecture: api.clinpgx.org for annotation records, api.cpicpgx.org for genotype→recommendation lookups. No auth. For germline pathogenicity use clinvar-database; for somatic cancer PGx use cosmic-database or opentargets-database; for drug bioactivity use chembl-database-bioactivity.
日本語の概要は準備中です。原文の説明を表示しています。
Query IUPHAR/BPS Guide to Pharmacology (GtoPdb) for receptor-ligand interactions, target/ligand metadata, families, and approved drugs. Affinities (pKi/pIC50/pKd), action (Agonist/Antagonist/etc.), species, structures (SMILES/InChI). No auth. Always resolve targets via geneSymbol/accession; most metadata lives in sub-resources (/databaseLinks, /structure, /synonyms).
日本語の概要は準備中です。原文の説明を表示しています。
Query DDInter drug-drug interactions via REST API (1.7M+ interactions, 2,400+ drugs). Search by drug name/ID for severity (major/moderate/minor), mechanisms, and clinical recommendations. No auth. For FDA labeling use dailymed-database; for pharmacogenomics use clinpgx-database.
日本語の概要は準備中です。原文の説明を表示しています。
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
日本語の概要は準備中です。原文の説明を表示しています。
Guidelines for clinical decision support (CDS) documents: biomarker-stratified cohort analyses and GRADE-graded treatment reports. Covers structure, executive summaries, evidence grading (1A–2C), stats (HR, CI, survival), and biomarker integration. Use for pharma research docs, clinical guidelines, regulatory submissions.
日本語の概要は準備中です。原文の説明を表示しています。
Med-adapted earnings preview: consensus estimates, historical surprises, guidance sensitivities, and the FDA-catalyst overlay (PDUFA/AdCom/trial readouts near the print) for biotech/pharma names.
日本語の概要は準備中です。原文の説明を表示しています。
Med peer benchmarking: select actual biotech/pharma peers via the med universe (drug/indication overlap where possible) and compare med-relevant metrics — pipeline depth, catalyst density, cash position, margins, valuation multiples.
日本語の概要は準備中です。原文の説明を表示しています。
Med-sector overview across med.medicines_biotech and med.medical_devices with med trends, catalysts, FDA-decision context, and a cross-sector ripple map (quantified sign/magnitude per name). Use to frame any biotech/pharma/device analysis before diving into a single name.
日本語の概要は準備中です。原文の説明を表示しています。
CDER-track catalyst analysis for medicines: PDUFA target dates, AdCom votes, sNDA/sBLA label expansions, biosimilar interchangeability and 180-day exclusivity, Orange Book state, and CRL remediation timelines — enumerated, verified, and sized per event for any biotech/pharma ticker or watchlist.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Medical Geneticist. Use when a task calls for Medical Geneticist judgment. Reasons from pedigree priors, HPO phenotype match, and ACMG/ClinGen variant classification; integrates exome/genome, CMA, RNA splicing, NBS ACT pathways, Tier 3 carrier screening, SF v3.3, and CPIC pharmacogenomics while treating VUS overcall, CPM/NIPT discordance, mtDNA heteroplasmy sampling, and SpliceAI-only splicing claims as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。