'pharma' 검색 결과

Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Think and work like an expert Clinical Pharmacologist. Use when a task calls for Clinical Pharmacologist judgment. Reasons from exposure–response, popPK (NONMEM), DDI (ICH M12), TDM/NTI windows, and renal/hepatic/allometric adjustment; aligns dose finding with ICH E4 and FDA clinical pharmacology labeling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious-case filtering, suspect-drug and concomitant-exclusion logic, reporting odds ratio analysis, comparator-drug benchmarking, cross-drug comparison, and cautious signal interpretation without causal overclaiming. Covers five study patterns (single-drug disproportionality workflow, multi-drug class comparison workflow, indication-stratified workflow, comparator-controlled signal screening workflow...

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

RxMemberSim generates realistic synthetic pharmacy data for testing PBM systems, claims adjudication, and drug utilization review. Use when user requests: (1) pharmacy claims or prescription data, (2) DUR alerts or drug interactions, (3) formulary or tier cohorts, (4) pharmacy prior authorization, (5) NCPDP formatted output.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate Claim, ClaimResponse, and ExplanationOfBenefit (EOB) resources for healthcare billing including professional, institutional, pharmacy (Rx), dental, and vision claims. Use when user mentions claims, EOB, explanation of benefit, billing, reimbursement, adjudication, pharmacy claims, Rx claims, denied claims, copay, deductible, or allowed amount.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement pharmaceutical serialisation and track-and-trace systems compliant with EU FMD, US DSCSA, and other global regulations. Covers unique identifier generation, aggregation hierarchy, EPCIS data exchange, and verification endpoint integration. Use when implementing serialisation for a new product launch, integrating with the EMVS/NMVS, designing DSCSA-compliant transaction exchange, building an EPCIS event repository, or extending serialisation to additional markets (China, Brazil, Russia).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Select the right pharmacologic agent for a child aged 12+ with obesity using the CMAJ 2025 guideline — choosing between GLP-1 receptor agonists, metformin, or orlistat with monitoring guidance. Trigger when a clinician asks which medication to use for pediatric obesity, whether to start semaglutide or metformin in a child, or how to manage obesity pharmacologically in adolescents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level medicinal chemistry knowledge. Use when working with drug design, structure-activity relationships, ADMET properties, pharmacophores, lead optimization, target identification, drug metabolism, or pharmaceutical development. Also use when the user mentions 'SAR', 'drug design', 'pharmacophore', 'ADMET', 'lead compound', 'hit to lead', 'drug metabolism', 'bioavailability', 'selectivity', 'binding affinity', 'scaffold hopping', or 'bioisostere'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes/comments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA/461 fallback to Agent Reach/OpenCLI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA pharmacogenomic allele interpretation, and clinically-actionable PGx report generation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

生成 A 股医药行业日报：从新浪财经抓取 20 只核心医药股实时行情，分析 7 大细分赛道排名、涨跌榜 TOP 3、资金流向，然后通过飞书发送富文本日报。 覆盖恒瑞医药、百济神州、智飞生物、药明康德、迈瑞医疗、片仔癀、云南白药等 20 只核心标的。 当用户需要 医药日报、医药行业日报、pharma daily report、 新浪财经医药数据、A 股医药板块分析、飞书推送医药数据、 制药行业快报 时触发。也适用于"帮我看一下今天医药板块"、 "给飞书发一份今天的医药数据"等口语化请求。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Queries PharmGKB / CPIC / DPWG for drug-gene interactions; calls CYP2D6/CYP2C9/CYP2C19/DPYD/TPMT/NUDT15/UGT1A1/SLCO1B1 star alleles and phenotype with PharmCAT, Cyrius (CYP2D6 structural variants), Aldy, Stargazer; applies Caudle 2020 activity-score translation. Use when implementing pharmacogenomic-guided prescribing, applying CPIC vs DPWG guidance, screening HLA risk alleles for ICI / antiepileptics / abacavir, or interpreting compound TPMT+NUDT15 thiopurine risk.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Refill a prescription at a pharmacy. Works from a medication name, an Rx number, a photo of the bottle, or just "I'm running low." Confirms exactly what's being requested, gathers everything the pharmacy will ask for up front, and handles the refill online or by phone — whichever is fastest.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

HealthSim generates realistic synthetic healthcare data for testing EMR systems, claims processing, pharmacy benefits, and analytics. Use for ANY request involving: (1) synthetic patients, clinical data, or medical records, (2) healthcare claims, billing, or adjudication, (3) pharmacy prescriptions, formularies, or drug utilization, (4) HL7v2, FHIR, X12, or NCPDP formatted output, (5) healthcare testing scenarios or sample data generation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

HealthSim generates realistic synthetic healthcare data for testing EMR systems, claims processing, pharmacy benefits, and analytics. Use for ANY request involving: (1) synthetic patients, clinical data, or medical records, (2) healthcare claims, billing, or adjudication, (3) pharmacy prescriptions, formularies, or drug utilization, (4) HL7v2, FHIR, X12, or NCPDP formatted output, (5) healthcare testing scenarios or sample data generation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

HealthSim generates realistic synthetic healthcare data for testing EMR systems, claims processing, pharmacy benefits, and analytics. Use for ANY request involving: (1) synthetic patients, clinical data, or medical records, (2) healthcare claims, billing, or adjudication, (3) pharmacy prescriptions, formularies, or drug utilization, (4) HL7v2, FHIR, X12, or NCPDP formatted output, (5) healthcare testing scenarios or sample data generation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.