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Deepen RDKit tutorial notebooks #4

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897b86b
Enhance advanced RDKit tutorials
suneelbvs Oct 13, 2025
ed5ad52
Update 8_QSAR_Modeling_with_Scikit_Learn.ipynb
suneelbvs Oct 13, 2025
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151 changes: 151 additions & 0 deletions 10_Chemical_Format_Conversion_and_Metadata.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial 10: Chemical Format Conversion and Metadata Handling\n",
"\n",
"Round-trip molecules between common chemical formats while preserving metadata fields.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Objectives\n",
"\n",
"- Load structure-data files (SDF) that contain rich metadata.\n",
"- Convert the records into pandas DataFrames for analysis.\n",
"- Export SMILES and SDF files with selected properties.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"from rdkit import Chem\n",
"from rdkit.Chem import PandasTools\n",
"import pandas as pd\n",
"from io import StringIO\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read SDF Data\n",
"\n",
"The snippet below emulates reading from disk by loading a multi-record SDF string.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"sdf_block = \"\"\"\n",
" Mrv2108 07152116512D\n",
"\n",
" 6 5 0 0 0 0 999 V2000\n",
" 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1 2 2 0 0 0 0\n",
" 2 3 1 0 0 0 0\n",
" 3 4 2 0 0 0 0\n",
" 4 5 1 0 0 0 0\n",
" 5 6 2 0 0 0 0\n",
" 6 1 1 0 0 0 0\n",
"M END\n",
"> <Name>\n",
"Benzene\n",
"\n",
"> <Source>\n",
"Example\n",
"\n",
"$$$$\n",
"\"\"\".strip()\n",
"supplier = Chem.SDMolSupplier()\n",
"supplier.SetData(sdf_block, sanitize=True)\n",
"molecules = [mol for mol in supplier if mol is not None]\n",
"len(molecules)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert to a DataFrame\n",
"\n",
"`PandasTools.LoadSDF` retains all metadata fields, making downstream analytics straightforward.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"sdf_buffer = StringIO(sdf_block)\n",
"df = PandasTools.LoadSDF(sdf_buffer, smilesName='smiles', molColName='ROMol')\n",
"df\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write SMILES and SDF Outputs\n",
"\n",
"Export the curated data to SMILES or SDF files. String buffers allow inspection without touching disk.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"smiles_buffer = StringIO()\n",
"PandasTools.WriteSmi(df, smiles_buffer, molColName='ROMol', includeHeader=True, idName='Name')\n",
"smiles_buffer.getvalue()\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"sdf_writer = Chem.SDWriter()\n",
"sdf_output = StringIO()\n",
"sdf_writer.SetOutputStream(sdf_output)\n",
"for mol in molecules:\n",
" mol.SetProp('Processed', 'True')\n",
" sdf_writer.write(mol)\n",
"sdf_writer.close()\n",
"sdf_output.getvalue().splitlines()[:10]\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
160 changes: 160 additions & 0 deletions 5_Conformer_Generation_and_3D_Analysis.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial 5: 3D Conformer Generation and Analysis\n",
"\n",
"Learn how to generate three-dimensional conformers for a molecule, optimise their geometry, and compare the resulting ensemble.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Objectives\n",
"\n",
"- Prepare a molecule with explicit hydrogens so that the force field has the atoms it expects.\n",
"- Embed several conformers with the ETKDG algorithm and perform force-field minimisation.\n",
"- Analyse conformer energies and pairwise RMS values to identify the most representative structures.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem, Draw\n",
"from rdkit.Chem import rdMolAlign\n",
"import pandas as pd\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare an Example Molecule\n",
"\n",
"We will work with ibuprofen, a small drug-like molecule that exhibits several low-energy conformations.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"ibuprofen = Chem.AddHs(Chem.MolFromSmiles('CC(C)Cc1ccc(cc1)[C@@H](C)C(=O)O'))\n",
"ibuprofen\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Conformers with ETKDG\n",
"\n",
"The experimental torsion knowledge distance geometry (ETKDG) method provides a robust starting point for 3D coordinates.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"params = AllChem.ETKDGv3()\n",
"params.randomSeed = 0xF00D\n",
"conformer_ids = list(AllChem.EmbedMultipleConfs(ibuprofen, numConfs=10, params=params))\n",
"print(f\"Generated {len(conformer_ids)} conformers\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimise with a Force Field\n",
"\n",
"Each conformer is refined with the Universal Force Field (UFF). The final energy (in kcal/mol) helps rank conformers.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"energy_records = []\n",
"for cid in conformer_ids:\n",
" AllChem.UFFOptimizeMolecule(ibuprofen, confId=cid)\n",
" ff = AllChem.UFFGetMoleculeForceField(ibuprofen, confId=cid)\n",
" energy_records.append((cid, ff.CalcEnergy()))\n",
"energy_df = pd.DataFrame(energy_records, columns=['conformer_id', 'uff_energy_kcal'])\n",
"energy_df.sort_values('uff_energy_kcal').reset_index(drop=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare Conformer Geometries\n",
"\n",
"The RMS distance matrix quantifies structural differences between conformers. Smaller values indicate similar geometries.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"rms_matrix = AllChem.GetConformerRMSMatrix(ibuprofen, prealigned=False)\n",
"rms_df = pd.DataFrame(\n",
" data=rms_matrix,\n",
" columns=[f\"conf_{i}\" for i in conformer_ids[1:]],\n",
" index=[f\"conf_{i}\" for i in conformer_ids[:-1]]\n",
")\n",
"rms_df.round(3)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualise the Lowest-Energy Conformers\n",
"\n",
"Drawing the lowest-energy conformers helps communicate which geometry the force field prefers.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"ranked = energy_df.sort_values('uff_energy_kcal').head(4)['conformer_id'].tolist()\n",
"mols = [Chem.Mol(ibuprofen) for _ in ranked]\n",
"for new_conf, cid in zip(mols, ranked):\n",
" new_conf.RemoveAllConformers()\n",
" new_conf.AddConformer(ibuprofen.GetConformer(id=cid), assignId=True)\n",
"Draw.MolsToGridImage([Chem.RemoveHs(m) for m in mols], legends=[f\"conf {cid}\" for cid in ranked], molsPerRow=2)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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