Results for “drug-likeness”

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

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

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

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

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.

Assess pharmacotherapy eligibility based on BMI/comorbidity

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

Access the ZINC database of 230M+ commercially available (purchasable) compounds, searching by ZINC ID or SMILES, running similarity searches, and downloading 3D-ready structures. Use when assembling a compound library for virtual screening, finding purchasable analogs, or obtaining docking-ready 3D structures for drug discovery. 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.

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.

Advises assessing efficacy and safety at least monthly for the first 3 months, then at least every 3 months for all patients prescribed weight‑loss medications. Triggers include clinician questions such as “How often should I check progress after starting orlistat?” or “What is the follow‑up schedule for a patient on liraglutide?”.

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

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.

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

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

Guides dose escalation for obesity pharmacotherapy based on efficacy and tolerability while staying within approved upper dose limits. Triggers include clinician questions like "How should I titrate naltrexone/bupropion if the starting dose is tolerated?" or "What is the maximum liraglutide dose I can use?"

Simulates a NeurIPS/SC/ICSE-style peer review with five reviewer personas (HPC, ML, Stats, Reproducibility, Devil's Advocate) that verify every claim against actual result data before submission.

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.

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.

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

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.

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.

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.

Recommends weight‑losing and weight‑neutral medications as first‑ and second‑line agents for managing overweight/obese patients with type 2 diabetes. Triggers include when a clinician asks, 'Which diabetes medications will not worsen weight in this obese patient?' or 'Should I avoid sulfonylureas in this patient with T2DM and obesity due to weight gain risk?'

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

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

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

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

Recommends using weight‑neutral antipsychotic alternatives when possible and employing shared decision‑making that provides quantitative estimates of expected weight effect to guide drug choice. Triggered when a clinician asks, “How do I involve this patient in choosing an antipsychotic with minimal weight gain?” or “What resources show weight‑change projections for risperidone vs aripiprazole?”.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.