[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DM1XGCCCEW4RRBVDFWEKDW":3,"skill-same:01M6DM1XGCCCEW4RRBVDFWEKDW":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6DM1XGCCCEW4RRBVDFWEKDW","molecular-property-profiling",null,"Comprehensive molecular property analysis covering basic info, hydrophobicity, H-bonding, structural complexity, topology, drug-likeness, charge distribution, and complexity metrics.","InternScience\u002FDrClaw","InternScience","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F154449623?v=4","01M6DHY1ZF02QASNAVMNPT7NP6","Intern Science","01M6DHY2523FY56Q5572NKAAD0",172,"MIT","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fdrug\u002Fmolecular-property-profiling\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34],"profiling","molecular","molecular property profiling","metrics","topology","analysis","complexity","info","structural","distribution","basic","2026-11-10 10:12:30",false,"---\nname: molecular-property-profiling\ndescription: Comprehensive molecular property analysis covering basic info, hydrophobicity,\n  H-bonding, structural complexity, topology, drug-likeness, charge distribution,\n  and complexity metrics.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 分子属性全面分析。\n---\n\n# Molecular Property Profiling Workflow\n\n## Usage\n\n### 1. MCP Server Definition\n\nUse the same `DrugSDAClient` class as defined in previous skills.\n\n### 2. Comprehensive Molecular Property Analysis\n\nThis workflow computes a comprehensive set of molecular descriptors across 8 different categories, providing a complete molecular profile for QSAR modeling, drug discovery, and molecular analysis.\n\n**Workflow Steps:**\n\n1. **Basic Properties** - Molecular formula, weight, atom counts, bond counts\n2. **Hydrophobicity** - LogP, molar refractivity, lipophilicity descriptors\n3. **Hydrogen Bonding** - H-bond donors\u002Facceptors, TPSA\n4. **Structural Complexity** - Ring counts, aromatic rings, rotatable bonds\n5. **Topological Descriptors** - Chi indices, Kappa shape indices\n6. **Drug Chemistry** - QED score, Lipinski violations\n7. **Charge Properties** - Gasteiger charges, formal charge\n8. **Complexity Metrics** - Molecular complexity, asphericity\n\n**Implementation:**\n\n```python\nfrom collections import defaultdict\n\ndef merge_lists_by_smiles(*lists):\n    \"\"\"Merge multiple descriptor lists by SMILES key\"\"\"\n    merged = defaultdict(dict)\n    for lst in lists:\n        for d in lst:\n            smiles = d['smiles']\n            merged[smiles].update(d)\n    return list(merged.values())\n\nclient = DrugSDAClient(\"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F2\u002FDrugSDA-Tool\")\nif not await client.connect():\n    print(\"connection failed\")\n    return\n\n## Input: List of SMILES strings\nsmiles_list = [\n    'Nc1nnc(S(=O)(=O)NCCc2ccc(O)cc2)s1',\n    'COc1ccc2c(=O)cc(C(=O)N3CCN(c4ccc(F)cc4)CC3)oc2c1',\n    'CCCC1CCC(CC(=O)Cl)(C2CCCCC2)CC1'\n]\n\n## Step 1: Calculate basic molecular properties\nresult = await client.session.call_tool(\n    \"calculate_mol_basic_info\",\n    arguments={\"smiles_list\": smiles_list}\n)\nbasic_metrics = client.parse_result(result)['metrics']\n\n## Step 2: Calculate hydrophobicity descriptors\nresult = await client.session.call_tool(\n    \"calculate_mol_hydrophobicity\",\n    arguments={\"smiles_list\": smiles_list}\n)\nhydrophobicity_metrics = client.parse_result(result)['metrics']\n\n## Step 3: Calculate hydrogen bonding properties\nresult = await client.session.call_tool(\n    \"calculate_mol_hbond\",\n    arguments={\"smiles_list\": smiles_list}\n)\nhbond_metrics = client.parse_result(result)['metrics']\n\n## Step 4: Calculate structural complexity\nresult = await client.session.call_tool(\n    \"calculate_mol_structure_complexity\",\n    arguments={\"smiles_list\": smiles_list}\n)\nstructure_metrics = client.parse_result(result)['metrics']\n\n## Step 5: Calculate topological descriptors\nresult = await client.session.call_tool(\n    \"calculate_mol_topology\",\n    arguments={\"smiles_list\": smiles_list}\n)\ntopology_metrics = client.parse_result(result)['metrics']\n\n## Step 6: Calculate drug chemistry properties\nresult = await client.session.call_tool(\n    \"calculate_mol_drug_chemistry\",\n    arguments={\"smiles_list\": smiles_list}\n)\nchemistry_metrics = client.parse_result(result)['metrics']\n\n## Step 7: Calculate charge properties\nresult = await client.session.call_tool(\n    \"calculate_mol_charge\",\n    arguments={\"smiles_list\": smiles_list}\n)\ncharge_metrics = client.parse_result(result)['metrics']\n\n## Step 8: Calculate complexity metrics\nresult = await client.session.call_tool(\n    \"calculate_mol_complexity\",\n    arguments={\"smiles_list\": smiles_list}\n)\ncomplexity_metrics = client.parse_result(result)['metrics']\n\n## Merge all descriptors by SMILES\ncomplete_profiles = merge_lists_by_smiles(\n    basic_metrics,\n    hydrophobicity_metrics,\n    hbond_metrics,\n    structure_metrics,\n    topology_metrics,\n    chemistry_metrics,\n    charge_metrics,\n    complexity_metrics\n)\n\n## Display results\nfor profile in complete_profiles:\n    print(f\"\\nSMILES: {profile['smiles']}\")\n    print(f\"Molecular Formula: {profile['molecular_formula']}\")\n    print(f\"Molecular Weight: {profile['molecular_weight']:.2f}\")\n    print(f\"LogP: {profile['logp']:.2f}\")\n    print(f\"QED Score: {profile['qed']:.4f}\")\n    print(f\"H-Bond Donors: {profile['num_h_donors']}\")\n    print(f\"H-Bond Acceptors: {profile['num_h_acceptors']}\")\n    print(f\"TPSA: {profile['tpsa']:.2f}\")\n    print(f\"Lipinski Violations: {profile['lipinski_rule_of_5_violations']}\")\n\nawait client.disconnect()\n```\n\n### Descriptor Categories\n\n#### 1. Basic Properties\n- `molecular_formula`: Molecular formula\n- `molecular_weight`: Molecular weight (Da)\n- `num_heavy_atoms`: Count of non-hydrogen atoms\n- `num_atoms`, `num_bonds`: Total atom and bond counts\n- `formal_charge`: Overall formal charge\n\n#### 2. Hydrophobicity\n- `logp`: Partition coefficient (lipophilicity)\n- `molar_refractivity`: Molar refractivity\n- `fraction_csp3`: Fraction of sp3 carbons (saturation)\n\n#### 3. Hydrogen Bonding\n- `num_h_donors`: H-bond donor count\n- `num_h_acceptors`: H-bond acceptor count\n- `tpsa`: Topological polar surface area (Ų)\n\n#### 4. Structural Complexity\n- `num_rings`, `num_aromatic_rings`: Ring counts\n- `num_rotatable_bonds`: Flexible bonds\n- `num_heteroatoms`: Non-C\u002FH atoms\n\n#### 5. Topological Descriptors\n- `chi0v`-`chi4v`: Chi connectivity indices\n- `kappa1`-`kappa3`: Kappa shape indices\n- `hall_kier_alpha`: Hall-Kier alpha value\n\n#### 6. Drug Chemistry\n- `qed`: Quantitative Estimate of Drug-likeness (0-1)\n- `lipinski_rule_of_5_violations`: Lipinski violations (0-4)\n\n#### 7. Charge Properties\n- `min\u002Fmax\u002Favg_gasteiger_charge`: Gasteiger partial charges\n- `gasteiger_charge_range`: Charge distribution range\n\n#### 8. Complexity Metrics\n- `molecular_complexity`: Bertz complexity index\n- `aromatic_proportion`: Fraction of aromatic atoms\n- `asphericity`: 3D shape asphericity\n\n### Input\u002FOutput\n\n**Input:**\n- `smiles_list`: List of SMILES strings\n\n**Output:**\n- List of dictionaries, each containing 50+ molecular descriptors for one molecule\n\n### Applications\n\n- **QSAR Modeling**: Use descriptors as features for predictive models\n- **Drug Discovery**: Screen compounds by drug-likeness and physicochemical properties\n- **Chemical Space Analysis**: Visualize and cluster molecules by properties\n- **Lead Optimization**: Track property changes during optimization\n- **Virtual Screening**: Filter libraries by desired property ranges\n\n### Property Filters for Drug-likeness\n\nTypical ranges for oral drug candidates:\n- Molecular Weight: 150-500 Da\n- LogP: 0-5\n- H-Bond Donors: ≤ 5\n- H-Bond Acceptors: ≤ 10\n- TPSA: 20-140 Ų\n- Rotatable Bonds: ≤ 10\n- QED Score: > 0.5\n","# Molecular Property Profiling Workflow\n\n## Usage\n\n### 1. MCP Server Definition\n\nUse the same `DrugSDAClient` class as defined in previous skills.\n\n### 2. Comprehensive Molecular Property Analysis\n\nThis workflow computes a comprehensive set of molecular descriptors across 8 different categories, providing a complete molecular profile for QSAR modeling, drug discovery, and molecular analysis.\n\n**Workflow Steps:**\n\n1. **Basic Properties** - Molecular formula, weight, atom counts, bond counts\n2. **Hydrophobicity** - LogP, molar refractivity, lipophilicity descriptors\n3. **Hydrogen Bonding** - H-bond donors\u002Facceptors, TPSA\n4. **Structural Complexity** - Ring counts, aromatic rings, rotatable bonds\n5. **Topological Descriptors** - Chi indices, Kappa shape indices\n6. **Drug Chemistry** - QED score, Lipinski violations\n7. **Charge Properties** - Gasteiger charges, formal charge\n8. **Complexity Metrics** - Molecular complexity, asphericity\n\n**Implementation:**\n\n```python\nfrom collections import defaultdict\n\ndef merge_lists_by_smiles(*lists):\n    \"\"\"Merge multiple descriptor lists by SMILES key\"\"\"\n    merged = defaultdict(dict)\n    for lst in lists:\n        for d in lst:\n            smiles = d['smiles']\n            merged[smiles].update(d)\n    return list(merged.values())\n\nclient = DrugSDAClient(\"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F2\u002FDrugSDA-Tool\")\nif not await client.connect():\n    print(\"connection failed\")\n    return\n\n## Input: List of SMILES strings\nsmiles_list = [\n    'Nc1nnc(S(=O)(=O)NCCc2ccc(O)cc2)s1',\n    'COc1ccc2c(=O)cc(C(=O)N3CCN(c4ccc(F)cc4)CC3)oc2c1',\n    'CCCC1CCC(CC(=O)Cl)(C2CCCCC2)CC1'\n]\n\n## Step 1: Calculate basic molecular properties\nresult = await client.session.call_tool(\n    \"calculate_mol_basic_info\",\n    arguments={\"smiles_list\": smiles_list}\n)\nbasic_metrics = client.parse_result(result)['metrics']\n\n## Step 2: Calculate hydrophobicity descriptors\nresult = await client.session.call_tool(\n    \"calculate_mol_hydrophobicity\",\n    arguments={\"smiles_list\": smiles_list}\n)\nhydrophobicity_metrics = client.parse_result(result)['metrics']\n\n## Step 3: Calculate hydrogen bonding properties\nresult = await client.session.call_tool(\n    \"calculate_mol_hbond\",\n    arguments={\"smiles_list\": smiles_list}\n)\nhbond_metrics = client.parse_result(result)['metrics']\n\n## Step 4: Calculate structural complexity\nresult = await client.session.call_tool(\n    \"calculate_mol_structure_complexity\",\n    arguments={\"smiles_list\": smiles_list}\n)\nstructure_metrics = client.parse_result(result)['metrics']\n\n## Step 5: Calculate topological descriptors\nresult = await client.session.call_tool(\n    \"calculate_mol_topology\",\n    arguments={\"smiles_list\": smiles_list}\n)\ntopology_metrics = client.parse_result(result)['metrics']\n\n## Step 6: Calculate drug chemistry properties\nresult = await client.session.call_tool(\n    \"calculate_mol_drug_chemistry\",\n    arguments={\"smiles_list\": smiles_list}\n)\nchemistry_metrics = client.parse_result(result)['metrics']\n\n## Step 7: Calculate charge properties\nresult = await client.session.call_tool(\n    \"calculate_mol_charge\",\n    arguments={\"smiles_list\": smiles_list}\n)\ncharge_metrics = client.parse_result(result)['metrics']\n\n## Step 8: Calculate complexity metrics\nresult = await client.session.call_tool(\n    \"calculate_mol_complexity\",\n    arguments={\"smiles_list\": smiles_list}\n)\ncomplexity_metrics = client.parse_result(result)['metrics']\n\n## Merge all descriptors by SMILES\ncomplete_profiles = merge_lists_by_smiles(\n    basic_metrics,\n    hydrophobicity_metrics,\n    hbond_metrics,\n    structure_metrics,\n    topology_metrics,\n    chemistry_metrics,\n    charge_metrics,\n    complexity_metrics\n)\n\n## Display results\nfor profile in complete_profiles:\n    print(f\"\\nSMILES: {profile['smiles']}\")\n    print(f\"Molecular Formula: {profile['molecular_formula']}\")\n    print(f\"Molecular Weight: {profile['molecular_weight']:.2f}\")\n    print(f\"LogP: {profile['logp']:.2f}\")\n    print(f\"QED Score: {profile['qed']:.4f}\")\n    print(f\"H-Bond Donors: {profile['num_h_donors']}\")\n    print(f\"H-Bond Acceptors: {profile['num_h_acceptors']}\")\n    print(f\"TPSA: {profile['tpsa']:.2f}\")\n    print(f\"Lipinski Violations: {profile['lipinski_rule_of_5_violations']}\")\n\nawait client.disconnect()\n```\n\n### Descriptor Categories\n\n#### 1. Basic Properties\n- `molecular_formula`: Molecular formula\n- `molecular_weight`: Molecular weight (Da)\n- `num_heavy_atoms`: Count of non-hydrogen atoms\n- `num_atoms`, `num_bonds`: Total atom and bond counts\n- `formal_charge`: Overall formal charge\n\n#### 2. Hydrophobicity\n- `logp`: Partition coefficient (lipophilicity)\n- `molar_refractivity`: Molar refractivity\n- `fraction_csp3`: Fraction of sp3 carbons (saturation)\n\n#### 3. Hydrogen Bonding\n- `num_h_donors`: H-bond donor count\n- `num_h_acceptors`: H-bond acceptor count\n- `tpsa`: Topological polar surface area (Ų)\n\n#### 4. Structural Complexity\n- `num_rings`, `num_aromatic_rings`: Ring counts\n- `num_rotatable_bonds`: Flexible bonds\n- `num_heteroatoms`: Non-C\u002FH atoms\n\n#### 5. Topological Descriptors\n- `chi0v`-`chi4v`: Chi connectivity indices\n- `kappa1`-`kappa3`: Kappa shape indices\n- `hall_kier_alpha`: Hall-Kier alpha value\n\n#### 6. Drug Chemistry\n- `qed`: Quantitative Estimate of Drug-likeness (0-1)\n- `lipinski_rule_of_5_violations`: Lipinski violations (0-4)\n\n#### 7. Charge Properties\n- `min\u002Fmax\u002Favg_gasteiger_charge`: Gasteiger partial charges\n- `gasteiger_charge_range`: Charge distribution range\n\n#### 8. Complexity Metrics\n- `molecular_complexity`: Bertz complexity index\n- `aromatic_proportion`: Fraction of aromatic atoms\n- `asphericity`: 3D shape asphericity\n\n### Input\u002FOutput\n\n**Input:**\n- `smiles_list`: List of SMILES strings\n\n**Output:**\n- List of dictionaries, each containing 50+ molecular descriptors for one molecule\n\n### Applications\n\n- **QSAR Modeling**: Use descriptors as features for predictive models\n- **Drug Discovery**: Screen compounds by drug-likeness and physicochemical properties\n- **Chemical Space Analysis**: Visualize and cluster molecules by properties\n- **Lead Optimization**: Track property changes during optimization\n- **Virtual Screening**: Filter libraries by desired property ranges\n\n### Property Filters for Drug-likeness\n\nTypical ranges for oral drug candidates:\n- Molecular Weight: 150-500 Da\n- LogP: 0-5\n- H-Bond Donors: ≤ 5\n- H-Bond Acceptors: ≤ 10\n- TPSA: 20-140 Ų\n- Rotatable Bonds: ≤ 10\n- QED Score: > 0.5\n",{"i18n":40,"name":5,"license":43,"metadata":44,"description":7},{"zh":41},{"description":42},"分子属性全面分析。","MIT license",{"skill-author":45},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fscience\u002Fdrug\u002Fmolecular-property-profiling",10,"https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw","https:\u002F\u002Fgithub.com\u002FInternScience","Shanghai Artificial Intelligence Laboratory","2026-03-20 17:06:10",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add InternScience\u002FDrClaw --skill molecular-property-profiling",{"tool":60,"command":61},"bunx","bunx skills add InternScience\u002FDrClaw --skill molecular-property-profiling",{"tool":63,"command":64},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill molecular-property-profiling","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fdrug\u002Fmolecular-property-profiling\u002FSKILL.md",true,[],[69,103,126,142,160,175],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":83,"publishedAt":35},"01M6DMQESTSBW8VJW03QWHMT85","scientific-critical-thinking","Evaluate scientific claims and evidence quality. 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