[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DMJMZ5WGB7DP7MKW7DZBRC":3,"skill-same:01M6DMJMZ5WGB7DP7MKW7DZBRC":72},{"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":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6DMJMZ5WGB7DP7MKW7DZBRC","molecular-descriptors-calculation",null,"Calculate advanced molecular descriptors including shape indices, connectivity indices, and structural features for QSAR and drug discovery.","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\u002Fprotein\u002Fmolecular-descriptors-calculation\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],"molecular","molecular descriptors calculation","discovery","计算","calculate","shape","connectivity","structural","drug discovery","2026-11-10 10:12:30",false,"---\nname: molecular-descriptors-calculation\ndescription: Calculate advanced molecular descriptors including shape indices, connectivity\n  indices, and structural features for QSAR and drug discovery.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 计算高级分子描述符。\n---\n\n# Molecular Descriptors Calculation\n\n## Usage\n\n### 1. MCP Server Definition\n\nUse the same `ChemicalToolsClient` class as defined in the molecular-properties-calculation skill.\n\n### 2. Molecular Descriptors Calculation Workflow\n\nThis workflow calculates advanced molecular descriptors used in QSAR modeling, drug discovery, and computational chemistry.\n\n**Workflow Steps:**\n\n1. **Calculate Kappa Shape Indices** - Molecular shape descriptors\n2. **Calculate Connectivity Indices** - Topological descriptors\n3. **Calculate Structural Features** - Rings, bonds, and functional groups\n\n**Implementation:**\n\n```python\n## Initialize client\nHEADERS = {\"SCP-HUB-API-KEY\": \"\u003Cyour-api-key>\"}\n\nclient = ChemicalToolsClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F31\u002FSciToolAgent-Chem\",\n    HEADERS\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\n## Input: SMILES string to analyze\nsmiles = \"CCO\"  # Ethanol\nprint(f\"=== Molecular Descriptors for {smiles} ===\\n\")\n\n## Step 1: Calculate Kappa shape indices\nprint(\"Step 1: Kappa Shape Indices\")\nfor tool in [\"GetKappa1\", \"GetKappa2\", \"GetKappa3\"]:\n    result = await client.client.call_tool(\n        tool,\n        arguments={\"smiles\": smiles}\n    )\n    result_data = client.parse_result(result)\n    print(f\"{tool}: {result_data}\")\nprint()\n\n## Step 2: Calculate Chi connectivity indices\nprint(\"Step 2: Chi Connectivity Indices\")\nfor tool in [\"GetChi0n\", \"GetChi0v\", \"GetChi1n\", \"GetChi1v\"]:\n    result = await client.client.call_tool(\n        tool,\n        arguments={\"smiles\": smiles}\n    )\n    result_data = client.parse_result(result)\n    print(f\"{tool}: {result_data}\")\nprint()\n\n## Step 3: Calculate structural features\nprint(\"Step 3: Structural Features\")\n\n# Rotatable bonds\nresult = await client.client.call_tool(\n    \"GetRotatableBondsNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Rotatable bonds: {client.parse_result(result)}\")\n\n# Hydrogen bond donors and acceptors\nresult = await client.client.call_tool(\n    \"GetHBDNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"H-bond donors: {client.parse_result(result)}\")\n\nresult = await client.client.call_tool(\n    \"GetHBANum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"H-bond acceptors: {client.parse_result(result)}\")\n\n# Ring counts\nresult = await client.client.call_tool(\n    \"GetRingsNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Number of rings: {client.parse_result(result)}\")\n\nresult = await client.client.call_tool(\n    \"GetAromaticRingsNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Aromatic rings: {client.parse_result(result)}\")\nprint()\n\n## Step 4: Calculate physicochemical descriptors\nprint(\"Step 4: Physicochemical Descriptors\")\n\n# LogP and molar refractivity (Crippen descriptors)\nresult = await client.client.call_tool(\n    \"GetCrippenDescriptors\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Crippen descriptors (LogP, MR): {client.parse_result(result)}\")\n\n# Topological polar surface area\nresult = await client.client.call_tool(\n    \"CalculateTPSA\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"TPSA: {client.parse_result(result)}\")\n\n# Fraction of sp3 carbons\nresult = await client.client.call_tool(\n    \"GetFractionCSP3\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Fraction sp3 carbons: {client.parse_result(result)}\")\nprint()\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**SciToolAgent-Chem Server:**\n\n**Shape Descriptors:**\n- `GetKappa1`, `GetKappa2`, `GetKappa3`: Kappa shape indices (molecular shape)\n\n**Connectivity Indices:**\n- `GetChi0n`, `GetChi0v`: Zero-order chi indices\n- `GetChi1n`, `GetChi1v`: First-order chi indices\n- `GetChi2n`, `GetChi2v`: Second-order chi indices\n- `GetChi3n`, `GetChi3v`, `GetChi4n`, `GetChi4v`: Higher-order chi indices\n\n**Structural Features:**\n- `GetRotatableBondsNum`: Count rotatable bonds (flexibility)\n- `GetHBDNum`\u002F`GetHBANum`: Hydrogen bond donors\u002Facceptors\n- `GetRingsNum`: Total ring count\n- `GetAromaticRingsNum`: Aromatic ring count\n- `GetAliphaticRingsNum`: Aliphatic ring count\n\n**Physicochemical Descriptors:**\n- `GetCrippenDescriptors`: LogP (lipophilicity) and molar refractivity\n- `CalculateTPSA`: Topological polar surface area\n- `GetFractionCSP3`: Fraction of sp³ hybridized carbons\n- `GetLabuteASA`: Labute accessible surface area\n\n### Input\u002FOutput\n\n**Input:**\n- `smiles`: Molecule in SMILES format\n\n**Output:**\n- **Kappa Indices**: Molecular shape descriptors (1, 2, 3)\n- **Chi Indices**: Topological connectivity indices\n- **Structural Counts**: Rings, bonds, functional groups\n- **LogP**: Lipophilicity (partition coefficient)\n- **TPSA**: Topological polar surface area (Ų)\n- **Fraction sp³**: Proportion of sp³ carbons (0-1)\n\n### Descriptor Applications\n\n**Kappa Shape Indices**\n- κ₁, κ₂, κ₃: Describe molecular shape from linear to spherical\n- Used in: QSAR models, molecular shape comparison\n\n**Chi Connectivity Indices**\n- Encode information about branching and cyclicity\n- Used in: Property prediction, similarity searching\n\n**Structural Features**\n- **Rotatable bonds**: Molecular flexibility, bioavailability\n- **H-bond donors\u002Facceptors**: Solubility, permeability\n- **Rings**: Rigidity, drug-likeness\n\n**Physicochemical Descriptors**\n- **LogP**: Lipophilicity, membrane permeability\n- **TPSA**: Oral bioavailability, BBB penetration\n- **Fraction sp³**: Molecular complexity, drug-likeness\n\n### Drug-Likeness Rules\n\n**Lipinski's Rule of Five:**\n- MW ≤ 500 Da\n- LogP ≤ 5\n- HBD ≤ 5\n- HBA ≤ 10\n\n**Veber's Rules (Oral Bioavailability):**\n- Rotatable bonds ≤ 10\n- TPSA ≤ 140 Ų\n\n**CNS Drug-Likeness:**\n- TPSA \u003C 90 Ų (for blood-brain barrier penetration)\n\n### Use Cases\n\n- QSAR model development\n- Virtual screening and compound prioritization\n- Drug-likeness assessment\n- Molecular similarity calculations\n- Pharmacokinetic property prediction\n- Lead optimization\n- Chemical space exploration\n\n### Additional Descriptor Tools\n\nThe SciToolAgent-Chem server provides 160+ tools including:\n- `GetBCUT`: BCUT descriptors\n- `GetAutocorrelation2D`\u002F`GetAutocorrelation3D`: Autocorrelation descriptors\n- `GetWHIM`: WHIM descriptors\n- `GetGETAWAY`: GETAWAY descriptors\n- `GetMORSE`: MORSE descriptors\n- `GetRDF`: Radial distribution function\n- `GetUSR`\u002F`GetUSRCAT`: Ultrafast shape recognition descriptors\n\n### Performance Notes\n\n- Most descriptor calculations are very fast (\u003C1 second)\n- Can batch process multiple molecules\n- Descriptors are deterministic (same molecule → same descriptors)\n","# Molecular Descriptors Calculation\n\n## Usage\n\n### 1. MCP Server Definition\n\nUse the same `ChemicalToolsClient` class as defined in the molecular-properties-calculation skill.\n\n### 2. Molecular Descriptors Calculation Workflow\n\nThis workflow calculates advanced molecular descriptors used in QSAR modeling, drug discovery, and computational chemistry.\n\n**Workflow Steps:**\n\n1. **Calculate Kappa Shape Indices** - Molecular shape descriptors\n2. **Calculate Connectivity Indices** - Topological descriptors\n3. **Calculate Structural Features** - Rings, bonds, and functional groups\n\n**Implementation:**\n\n```python\n## Initialize client\nHEADERS = {\"SCP-HUB-API-KEY\": \"\u003Cyour-api-key>\"}\n\nclient = ChemicalToolsClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F31\u002FSciToolAgent-Chem\",\n    HEADERS\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\n## Input: SMILES string to analyze\nsmiles = \"CCO\"  # Ethanol\nprint(f\"=== Molecular Descriptors for {smiles} ===\\n\")\n\n## Step 1: Calculate Kappa shape indices\nprint(\"Step 1: Kappa Shape Indices\")\nfor tool in [\"GetKappa1\", \"GetKappa2\", \"GetKappa3\"]:\n    result = await client.client.call_tool(\n        tool,\n        arguments={\"smiles\": smiles}\n    )\n    result_data = client.parse_result(result)\n    print(f\"{tool}: {result_data}\")\nprint()\n\n## Step 2: Calculate Chi connectivity indices\nprint(\"Step 2: Chi Connectivity Indices\")\nfor tool in [\"GetChi0n\", \"GetChi0v\", \"GetChi1n\", \"GetChi1v\"]:\n    result = await client.client.call_tool(\n        tool,\n        arguments={\"smiles\": smiles}\n    )\n    result_data = client.parse_result(result)\n    print(f\"{tool}: {result_data}\")\nprint()\n\n## Step 3: Calculate structural features\nprint(\"Step 3: Structural Features\")\n\n# Rotatable bonds\nresult = await client.client.call_tool(\n    \"GetRotatableBondsNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Rotatable bonds: {client.parse_result(result)}\")\n\n# Hydrogen bond donors and acceptors\nresult = await client.client.call_tool(\n    \"GetHBDNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"H-bond donors: {client.parse_result(result)}\")\n\nresult = await client.client.call_tool(\n    \"GetHBANum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"H-bond acceptors: {client.parse_result(result)}\")\n\n# Ring counts\nresult = await client.client.call_tool(\n    \"GetRingsNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Number of rings: {client.parse_result(result)}\")\n\nresult = await client.client.call_tool(\n    \"GetAromaticRingsNum\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Aromatic rings: {client.parse_result(result)}\")\nprint()\n\n## Step 4: Calculate physicochemical descriptors\nprint(\"Step 4: Physicochemical Descriptors\")\n\n# LogP and molar refractivity (Crippen descriptors)\nresult = await client.client.call_tool(\n    \"GetCrippenDescriptors\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Crippen descriptors (LogP, MR): {client.parse_result(result)}\")\n\n# Topological polar surface area\nresult = await client.client.call_tool(\n    \"CalculateTPSA\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"TPSA: {client.parse_result(result)}\")\n\n# Fraction of sp3 carbons\nresult = await client.client.call_tool(\n    \"GetFractionCSP3\",\n    arguments={\"smiles\": smiles}\n)\nprint(f\"Fraction sp3 carbons: {client.parse_result(result)}\")\nprint()\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**SciToolAgent-Chem Server:**\n\n**Shape Descriptors:**\n- `GetKappa1`, `GetKappa2`, `GetKappa3`: Kappa shape indices (molecular shape)\n\n**Connectivity Indices:**\n- `GetChi0n`, `GetChi0v`: Zero-order chi indices\n- `GetChi1n`, `GetChi1v`: First-order chi indices\n- `GetChi2n`, `GetChi2v`: Second-order chi indices\n- `GetChi3n`, `GetChi3v`, `GetChi4n`, `GetChi4v`: Higher-order chi indices\n\n**Structural Features:**\n- `GetRotatableBondsNum`: Count rotatable bonds (flexibility)\n- `GetHBDNum`\u002F`GetHBANum`: Hydrogen bond donors\u002Facceptors\n- `GetRingsNum`: Total ring count\n- `GetAromaticRingsNum`: Aromatic ring count\n- `GetAliphaticRingsNum`: Aliphatic ring count\n\n**Physicochemical Descriptors:**\n- `GetCrippenDescriptors`: LogP (lipophilicity) and molar refractivity\n- `CalculateTPSA`: Topological polar surface area\n- `GetFractionCSP3`: Fraction of sp³ hybridized carbons\n- `GetLabuteASA`: Labute accessible surface area\n\n### Input\u002FOutput\n\n**Input:**\n- `smiles`: Molecule in SMILES format\n\n**Output:**\n- **Kappa Indices**: Molecular shape descriptors (1, 2, 3)\n- **Chi Indices**: Topological connectivity indices\n- **Structural Counts**: Rings, bonds, functional groups\n- **LogP**: Lipophilicity (partition coefficient)\n- **TPSA**: Topological polar surface area (Ų)\n- **Fraction sp³**: Proportion of sp³ carbons (0-1)\n\n### Descriptor Applications\n\n**Kappa Shape Indices**\n- κ₁, κ₂, κ₃: Describe molecular shape from linear to spherical\n- Used in: QSAR models, molecular shape comparison\n\n**Chi Connectivity Indices**\n- Encode information about branching and cyclicity\n- Used in: Property prediction, similarity searching\n\n**Structural Features**\n- **Rotatable bonds**: Molecular flexibility, bioavailability\n- **H-bond donors\u002Facceptors**: Solubility, permeability\n- **Rings**: Rigidity, drug-likeness\n\n**Physicochemical Descriptors**\n- **LogP**: Lipophilicity, membrane permeability\n- **TPSA**: Oral bioavailability, BBB penetration\n- **Fraction sp³**: Molecular complexity, drug-likeness\n\n### Drug-Likeness Rules\n\n**Lipinski's Rule of Five:**\n- MW ≤ 500 Da\n- LogP ≤ 5\n- HBD ≤ 5\n- HBA ≤ 10\n\n**Veber's Rules (Oral Bioavailability):**\n- Rotatable bonds ≤ 10\n- TPSA ≤ 140 Ų\n\n**CNS Drug-Likeness:**\n- TPSA \u003C 90 Ų (for blood-brain barrier penetration)\n\n### Use Cases\n\n- QSAR model development\n- Virtual screening and compound prioritization\n- Drug-likeness assessment\n- Molecular similarity calculations\n- Pharmacokinetic property prediction\n- Lead optimization\n- Chemical space exploration\n\n### Additional Descriptor Tools\n\nThe SciToolAgent-Chem server provides 160+ tools including:\n- `GetBCUT`: BCUT descriptors\n- `GetAutocorrelation2D`\u002F`GetAutocorrelation3D`: Autocorrelation descriptors\n- `GetWHIM`: WHIM descriptors\n- `GetGETAWAY`: GETAWAY descriptors\n- `GetMORSE`: MORSE descriptors\n- `GetRDF`: Radial distribution function\n- `GetUSR`\u002F`GetUSRCAT`: Ultrafast shape recognition descriptors\n\n### Performance Notes\n\n- Most descriptor calculations are very fast (\u003C1 second)\n- Can batch process multiple molecules\n- Descriptors are deterministic (same molecule → same descriptors)\n",{"i18n":38,"name":5,"license":41,"metadata":42,"description":7},{"zh":39},{"description":40},"计算高级分子描述符。","MIT 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