Results for “pharmaceutical”

Query the ClinPGx API for pharmacogenomic gene-drug data, clinical annotations, CPIC guidelines, and FDA drug labels.

Queries the ClinPGx REST API for pharmacogenomic gene-drug data, clinical annotations, CPIC guidelines, and FDA drug labels, generating markdown reports with CSV tables.

Query the openFDA API for drugs, medical devices, adverse event reports, recalls, regulatory submissions (510k, PMA), and substance identification (UNII). Use when searching FDA safety data, pharmacovigilance and adverse-event signals, device clearances, drug labels, or recall records for regulatory data analysis and safety research. Part of the AlterLab Academic Skills suite.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Filtros de química medicinal. Aplique regras de similaridade a fármacos (Lipinski, Veber), filtros PAINS, alertas estruturais, métricas de complexidade, para priorização de compostos e filtragem de bibliotecas.

Access ClinPGx pharmacogenomics data (the successor to PharmGKB) to query gene-drug interactions, CPIC/DPWG dosing guidelines, drug labels, and pharmacogene records. Use when interpreting pharmacogenes (CYP2D6, CYP2C19, TPMT, DPYD, SLCO1B1), looking up genotype-guided drug dosing, checking PGx drug-safety associations (e.g. HLA-B*57:01 and abacavir), or supporting precision medicine and clinical pharmacogenomics decisions. For star-allele definitions/frequencies see PharmVar; for germline/somatic variant pathogenicity see alterlab-clinvar. Part of the AlterLab Academic Skills suite.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Identifies a medication from a photo and generates a genotype-informed dosage card using CPIC guidelines and real 23andMe data.

Therapeutics Data Commons. Conjuntos de dados prontos para IA em descoberta de drogas (ADME, toxicidade, DTI), benchmarks, divisões de scaffold, oráculos moleculares, para ML terapêutico e predição farmacológica.

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Filters and prioritizes compound libraries in drug discovery using drug-likeness rules, structural alerts, complexity metrics, and a query language.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Access and analyze drug information from the DrugBank database — drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. Use when working with pharmaceutical data, drug discovery research, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task needing detailed drug and drug-target records from DrugBank. Part of the AlterLab Academic Skills suite.

Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to a molecular-ML skill (e.g. deepchem). Part of the AlterLab Academic Skills suite.

Assess pharmacotherapy eligibility based on BMI/comorbidity

Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Apply medicinal chemistry filters for compound triage: drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and a custom query language for library filtering.

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound library, flagging PAINS or reactive groups, or assessing drug-likeness of candidate molecules. Part of the AlterLab Academic Skills suite.

Query the Open Targets Platform GraphQL API for target-disease associations, tractability and safety data, genetics/omics evidence, and known drugs. Use when identifying or prioritizing therapeutic drug targets, assessing target druggability/safety, or gathering target-disease evidence for drug discovery. Part of the AlterLab Academic Skills suite.

We need to output a SKILL.md file with the specified format.

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries.

Analyze product health across acquisition, activation, engagement, retention, quality, and monetization.

Bedside prescribing reference for Bemdec (bempedoic acid / NEXLETOL) — indication check, dose, statin co-prescribing safety caps, monitoring plan, warnings for hyperuricemia and tendon rupture, and special population guidance. Use when a clinician asks "can I start bempedoic acid", "Bemdec indication", "is bempedoic acid safe with my statin", "patient on Nexletol has joint pain or high uric acid", "bempedoic acid in CKD or liver disease or pregnancy", or any prescribing or monitoring question about bempedoic acid.

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

Provides a concise overview of CVS Health's history, business model, competitive moat, and key metrics.

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

This skill suggests using approved weight‑loss medication over no pharmacological therapy to ameliorate comorbidities and improve physical activity in adults with BMI ≥30 kg/m² or BMI ≥27 kg/m² with at least one comorbid condition. It is triggered when a clinician asks, for example, 'Should I add medication to help maintain weight loss in this patient with BMI 31 and diabetes?' or 'Is pharmacotherapy appropriate for long‑term weight control in a patient with BMI 28 and hypertension?'.