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Based on MolRAG (Xian 2025, ACL).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fmolecular-rag\u002FSKILL.md",[],[242,225,243,244,245,246,247,248,249,250,227,251,252,253,254,255,229,256,257,258],"rag","molecular rag","search","pandas","pypi","Optimization","rdkit","discovery","properties","chembl","drug-discovery","predictions","retrieval","2025","grounding","requests","analog-search",{"skillNo":260,"name":261,"nameZh":18,"description":262,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":263,"categories":264,"keywords":265,"publishedAt":234},"01M6XFHYA74RYR8BVATR8DANGC","molecular-optimization","Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fmolecular-optimization\u002FSKILL.md",[],[247,225,266,267,268,245,246,248,249,269,270,271,272,273,227,229,274,275,276,277],"molecular optimization","design","generate","verify","loop","analyze","numpy","paper","evaluate","ADMET","lead-optimization","molecular-design",{"skillNo":279,"name":280,"nameZh":18,"description":281,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":282,"categories":283,"keywords":284,"publishedAt":234},"01M6XFHQHHYHGVJDX9686ZQT1K","molecular-docking","End-to-end molecular docking pipeline. Target preparation, pocket detection, protein-ligand docking (DiffDock\u002FVina), scoring, interaction analysis, and pose ranking.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fmolecular-docking\u002FSKILL.md",[],[285,225,286,287,288,289,290,291,292,227,228,293,294,295,296,231,232,297],"docking","Molecular Docking","pipeline","detection","diffdock","analysis","scoring","end","molecular docking pipeline","ranking","preparation","pose","Pocket Detection",{"skillNo":299,"name":300,"nameZh":18,"description":301,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":302,"categories":303,"keywords":310,"publishedAt":234},"01M6XFH50EMB0TANP8B8FXE1BE","hypogenic","Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fhypogenic\u002FSKILL.md",[304,307],{"categoryNo":305,"categoryName":306,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":308,"categoryName":309,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000021","资料检索与研究",[300,311,312,290,313,314,315,316,227,317,318,319,320,321,322,323,324,325,326,327],"testing","data","hypothesis generation","explore","use","patterns","literature","scientific","insights","you","manual","for","data-driven","hypothesis formulation","content analysis","tabular","when",{"skillNo":329,"name":330,"nameZh":18,"description":331,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":332,"categories":333,"keywords":338,"publishedAt":234},"01M6XFGTXKZT58E3K41EJXXQDG","drug-design","End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdrug-design\u002FSKILL.md",[334,337],{"categoryNo":335,"categoryName":336,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":21,"categoryName":22,"taxonomyType":11,"taxonomyTypeDesc":12},[267,339,340,341,287,342,248,249,343,344,345,346,291,224,285,292,227,347,348,349,297],"Drug Design","orchestration","workflows","biopython","auto","python script","deterministic","scipy","de","prediction","Structure Prediction",{"skillNo":351,"name":289,"nameZh":18,"description":352,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":353,"categories":354,"keywords":355,"publishedAt":234},"01M6XFGKPXMJSCHA0A913ZEBTX","Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB\u002FSMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdiffdock\u002FSKILL.md",[],[289,267,356,246,342,248,249,357,358,224,359,285,225,227,360,361,362,322,348,231],"deep learning","learning","virtual","deep","virtual screening","pdb","screening",{"skillNo":364,"name":365,"nameZh":18,"description":366,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":367,"categories":368,"keywords":371,"publishedAt":234},"01M6XFGEXH90YD3TSF6YPTPX2Q","denovo-design","De novo molecule generation for drug discovery. Scaffold-based analog enumeration, fragment growing\u002Flinking, structure-based design, multi-objective optimization, and drug-likeness filtering.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdenovo-design\u002FSKILL.md",[369,370],{"categoryNo":335,"categoryName":336,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":21,"categoryName":22,"taxonomyType":11,"taxonomyTypeDesc":12},[267,372,217,247,373,249,374,224,375,227,376,347,377,378,322,379,380,381,382,383],"denovo design","enumeration","filtering","generation","drug discovery","multi","fragment","linking","objective","likeness","Structure-Based Design","multi-objective",{"skillNo":385,"name":386,"nameZh":18,"description":387,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":388,"categories":389,"keywords":390,"publishedAt":234},"01M6XFG8538D64STPGJZ1S2NNT","deepchem","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.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002FSKILL.md",[],[386,341,391,392,393,394,395,396,397,249,315,398,225,227,399,320,322,400,327,348],"experiments","neural networks","pytorch","benchmark","graph","networks","pytdc","options","quick","graph neural networks",{"skillNo":402,"name":403,"nameZh":18,"description":404,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":405,"categories":406,"keywords":407,"publishedAt":234},"01M6XFFSBC3KDVA67E6K8SR913","binding-affinity","Hybrid ML + physics binding affinity prediction. Empirical scoring, MM\u002FGBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fbinding-affinity\u002FSKILL.md",[],[408,409,410,246,342,248,249,358,411,346,291,412,227,360,413,362,322,414,348,231],"binding","Binding Affinity","batch","consensus","physics","mm","hybrid",{"skillNo":416,"name":417,"nameZh":18,"description":418,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":419,"categories":420,"keywords":421,"publishedAt":234},"01M6XFFM7AQ9593CDGGY3WJJFM","admet-reasoning","Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-reasoning\u002FSKILL.md",[],[422,423,424,245,246,290,248,249,425,426,272,427,227,252,428,255,229,429,275,430],"reasoning","admet reasoning","interpretation","park","maps","structural","pathways","2026","toxicity",{"skillNo":432,"name":433,"nameZh":18,"description":434,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":435,"categories":436,"keywords":437,"publishedAt":234},"01M6XFFG529NVVKDQ1CVWWM9YF","admet-prediction","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.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fadmet-prediction\u002FSKILL.md",[],[348,438,275,248,439,440,227,441,322,381,430],"admet prediction","models","full","panel",{"skillNo":443,"name":444,"nameZh":18,"description":445,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":446,"categories":447,"keywords":449,"publishedAt":234},"01M6XFEY3B4C2905VGXA8JY607","synthetic-biology","Synthetic biology design and simulation tools. Codon optimization, gene circuit ODE modeling with growth feedback, SBML model creation, bifurcation analysis, barcode sequencing fitness analysis, and therapeutic genome engineering. For metabolic modeling use cobrapy; for sequence tools use biopython.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fsynthetic-biology\u002FSKILL.md",[448],{"categoryNo":335,"categoryName":336,"taxonomyType":58,"taxonomyTypeDesc":59},[450,227,451,267,452,342,453,290,454,455,456,457,458,459,460,461,462,463,464,465,466],"synthetic biology","biology","Tools","cobrapy","simulation","model","feedback","modeling","creation","barcode","sequencing","circuit","sequence tools","gene","sequence","fitness","Bifurcation",{"skillNo":468,"name":469,"nameZh":18,"description":470,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":471,"categories":472,"keywords":473,"publishedAt":234},"01M6XFDCSNZBBXCPMXRQM541C6","pysam","Genomic file toolkit. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignments, VCF\u002FBCF variants, FASTA\u002FFASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpysam\u002FSKILL.md",[],[469,474,475,312,476,477,478,479,480,481,227,482,483,484,451,485,322,486,487,488],"toolkit","file","data processing","data processing pipelines","calculate","read","write","coverage","FASTQ","bam","vcf","processing","variants","sequences","FASTA",{"skillNo":490,"name":491,"nameZh":18,"description":492,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":493,"categories":494,"keywords":499,"publishedAt":234},"01M6XFD4F45GTXGS02YR56MV0K","pylabrobot","Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpylabrobot\u002FSKILL.md",[495,498],{"categoryNo":496,"categoryName":497,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":10,"categoryName":13,"taxonomyType":11,"taxonomyTypeDesc":12},[491,500,341,501,502,503,504,454,505,315,506,507,227,451,508,509,510,322,511,327],"automation","official","framework","protocols","api","programming","only","across","pumps","types","lab","plate",{"skillNo":513,"name":514,"nameZh":18,"description":515,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":516,"categories":517,"keywords":519,"publishedAt":234},"01M6XFCWVQTJY6TFYKKYSF2KB5","pyhealth","Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III\u002FIV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpyhealth\u002FSKILL.md",[518],{"categoryNo":496,"categoryName":497,"taxonomyType":58,"taxonomyTypeDesc":59},[514,311,520,356,521,522,312,523,524,525,359,526,527,227,528,451,529,530,327,348,531],"machine learning","tasks","gnn","healthcare","coding","signals","medical","clinical","medical coding","implementing","retain","NDC",{"skillNo":533,"name":534,"nameZh":18,"description":535,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":536,"categories":537,"keywords":538,"publishedAt":234},"01M6XFCA0S2BHME7736QVKZN7H","pydeseq2","Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano\u002FMA plots, for RNA-seq analysis.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpydeseq2\u002FSKILL.md",[],[534,539,290,540,541,542,227,543,544,545,347,451,546,547,548,322,463,549],"python","RNA","gene expression","tests","expression","DESeq2","volcano","rna-seq","bulk","seq","fdr",{"skillNo":551,"name":552,"nameZh":18,"description":553,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":554,"categories":555,"keywords":556,"publishedAt":234},"01M6XFBSY49AC4FFAVY4M6QZ26","pharmacology-wetlab","Computational analysis of pharmacology wet-lab experiments. Western blot densitometry, xenograft tumor growth inhibition, pharmaceutical stability modeling (Arrhenius), radiolabeled antibody biodistribution, MIRD dosimetry, and adverse event grading. For drug databases use chembl-database or fda-database; for molecular docking use diffdock.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpharmacology-wetlab\u002FSKILL.md",[],[557,558,559,391,560,289,290,561,315,562,563,285,564,457,225,251,451,565,510,322],"pharmacology","pharmacology wetlab","stability","database","growth","fda","databases","computational","event",10825,18]