'admet' 검색 결과

Predicts ADMET properties using ADMETlab 3.0 (119 platform features, including 77 prediction models with modeled-endpoint uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts ADMET properties using ADMETlab 3.0 (119 platform features, including 77 prediction models with modeled-endpoint uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction for drug candidates. Use for assessing drug-likeness, PK properties, and safety risks early in drug discovery. Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build molecular property prediction workflows with DeepChem for solubility, toxicity, and related chemistry tasks. Prepare molecular representations, choose compatible models, handle missing labels, and evaluate predictions using data splits designed for the intended scientific question.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting solubility from molecular structures
• Comparing graph models on MoleculeNet datasets
• Checking data splits for information leakage

Estimate small molecule solubility, permeability, and lipophilicity with Boltz using SMILES, a text notation for molecular structures. Process individual molecules or lists, review costs before submission, and receive results mapped to each input.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate properties for a molecule from its SMILES
• Evaluate a molecule list from a CSV file
• Compare predicted properties during initial screening

Screen an existing compound library against a protein target with Boltz. Rank candidates by binding confidence or lead optimization score, download predicted structures, and review solubility, permeability, and lipophilicity results alongside the ranked hits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening an existing compound library against a protein
• Finding candidates with high binding confidence
• Comparing candidates for lead optimization

Cheminformatics toolkit catalog for small molecules. Covers RDKit/OpenChemLib calculations, descriptors, fingerprints, similarity/substructure search, R-group, MMP, scaffold tree, chemical space, MPO, reactions, and ADMET predictions. Open this skill when the user asks about computing molecular properties, comparing/searching molecules, or predicting ADMET/tox.

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.

Small molecule identification, characterization, and procurement — PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, Enamine. Covers compound name to structure to activity to ADMET properties to commercial sourcing. Use for chemical biology, lead identification, probe selection, and the full small-molecule discovery pipeline.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment fitting. Use when you have plasma/serum drug concentrations over time after a dose and need PK parameters, or to compute bioavailability from IV + oral AUCs. NOT for ADMET property prediction from structure (use tooluniverse-admet-prediction).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

공개 데이터베이스에서 화합물 활성, 분자 특성, 약물 상호작용을 조사합니다. 경구 약물 후보의 특성과 유전자·질병 연관성을 정리해 신약 개발 연구를 지원합니다.

• 특정 표적에 작용하는 활성 화합물 탐색
• 경구 약물 후보의 분자 특성 평가
• 상호작용과 이상사례 보고 조사

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.