“drug discovery”的搜索结果

通过公开数据库查询化合物活性、分子性质和药物相互作用，整理候选口服药物的分子特征及基因与疾病的关联，为药物研发的前期调研提供支持。

• 查找针对特定靶点的活性化合物
• 评估候选口服药物的分子性质
• 调查相互作用和不良事件报告

Generate and screen small-molecule drug candidates with three NVIDIA BioNeMo NIM services. Use GenMol to create molecules, DiffDock to predict how they fit a target protein, and Boltz2 to estimate binding strength and rank candidates.

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• Generating and screening candidates without a starting scaffold
• Exploring analogs of a known molecular scaffold
• Screening a candidate library against a protein target

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.

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Query the FDA's public openFDA APIs for post-market drug data — FAERS adverse-event reports, Drugs@FDA approval and submission history, Structured Product Labels including boxed warnings, the National Drug Code directory, recall enforcement reports, and drug shortages. Use this skill to check what a regulator has already concluded about a molecule or its class, to date an approval and count its efficacy supplements, to read an approved indication or boxed warning, and to score a drug-event pair for disproportionate reporting with PRR, ROR, and chi-squared. Also trigger on openFDA, api.fda.gov, FAERS, Drugs@FDA, SPL, NDC, pharmacovigilance, boxed warning, adverse event report, drug recall, or safety signal.

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KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.

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Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target hypothesis generation, druggable-fraction analysis of disease loci, and human-genetics-validated drug-repurposing prioritization.

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Get oriented with a biological research plugin by checking connected research tools and reviewing available analysis skills. Receive a connection summary, optional setup guidance, and suggested starting points for literature searches, sequencing analysis, drug discovery, or research planning.

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• Getting started with the biological research plugin
• Checking connections to literature and drug discovery tools
• Reviewing available sequencing analysis skills

Build PyTorch machine learning workflows for molecules, proteins, and biomedical relationships. Get guidance on datasets, model selection, training, and evaluation for property prediction, protein modeling, molecule generation, and planning synthesis routes from target molecules.

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• Predicting molecular solubility and toxicity
• Predicting protein function from sequences or structures
• Exploring new drug uses through biomedical relationships

Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.

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Query the Open Targets Platform GraphQL API for target-disease associations, genetic and clinical evidence, tractability and safety liabilities, target prioritisation metrics, known drugs and mechanisms of action, and disease ontology. Use this skill for target identification and validation, target-disease evidence review, druggability assessment, drug repurposing, and resolving gene, disease, and drug names to Ensembl, MONDO, and ChEMBL identifiers. Also trigger when a query mentions Open Targets, platform.opentargets.org, association scores, tractability buckets, or api.platform.opentargets.org.

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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

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Screen compound libraries for drug discovery using drug-likeness guidelines, structural alerts that flag patterns for review, and molecular complexity measures. Combine criteria to shortlist candidates and retain filtering results with reasons for further assessment.

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• Screen compound libraries with drug-likeness rules
• Flag PAINS and other structures for review
• Combine property, structure, and complexity filters

Screen compound collections for drug discovery using established drug-likeness rules, structural warnings, and molecular complexity measures. Detect relevant chemical groups, apply property limits, and generate filtering results to support compound selection and prioritization.

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• Screening compound collections for drug-like candidates
• Detecting potentially problematic structures
• Prioritizing compounds during lead optimization

Decide whether a protein has a pocket worth targeting, and where it is, before committing to a docking or design campaign. Use this skill to run fpocket cavity detection, rank cavities by druggability and volume, compare apo and holo conformations to spot induced fit, identify allosteric and cryptic cavities that only open in simulation, and convert a chosen cavity into the search box coordinates a docking run needs. Also trigger on fpocket, cavity detection, druggability score, alpha sphere, cryptic pocket, allosteric site, pocket volume, hotspot mapping, or undruggable target assessment.

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Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological relationships across genes and proteins, drugs, diseases, phenotypes, pathways, biological processes, exposures and anatomy. Use this skill to search entities by name, pull direct neighbours and their evidence types, summarise the local network around a disease, and find direct or two-hop drug-disease connections for repurposing hypotheses. Also trigger on PrimeKG, kg.csv, Harvard Dataverse knowledge graph, disease_protein, drug_protein, indication and contraindication edges, or network pharmacology over a biomedical knowledge graph.

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Multi-method binding pocket detection and druggability assessment. Grid-based, fpocket, and P2Rank detection with druggability scoring, visualization, and cross-structure comparison.

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Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery workflows.

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• Load and standardize molecular datasets
• Calculate molecular properties and similarities
• Cluster compounds and select diverse subsets

Search ChEMBL for compounds by name, chemical structure, or properties. Retrieve biological activity measurements, target information, and drug mechanisms to investigate inhibitors and compare how structural differences relate to measured activity.

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• Finding inhibitors for a specific protein target
• Searching for compounds with similar structures
• Comparing chemical structures and biological activity

Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.

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PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.

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Patterns for computer-aided drug discovery including virtual screening, molecular docking, ADMET prediction, lead optimization, and integration with AI/ML methods. Covers both structure-based and ligand-based approaches. Use when ", " mentioned.

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Estimate how likely a protein therapeutic is to provoke an anti-drug antibody response, and locate the sequence regions responsible. Use this skill to tile a sequence into peptides, predict class II MHC presentation across a population-representative allele panel, aggregate predicted binders into a per-region and whole-molecule risk score, compare a candidate against its closest human germline, and decide which liabilities are worth deimmunising. Also trigger on immunogenicity, anti-drug antibody, ADA, T-cell epitope, MHC class II, HLA-DRB1, NetMHCIIpan, NetMHCpan, deimmunisation, tregitope, or population coverage.

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Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

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Audit a drug discovery pipeline for operational efficiency and scientific rigor. Triggers: building or reviewing pharma R&D platforms, cheminformatics pipelines, or screening data management systems.

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