rdkit

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Build molecules with RDKit — from SMILES or a scaffold, analogues and series (each with its parent), properties (MW, cLogP, TPSA, Lipinski, Veber, QED, alerts), similarity and substructure search, conformer ensembles written as SDF for the pane. Use for any request that ends in a molecule, a property or a chemical series.

AI & Automation 957 stars 79 forks Updated today MIT

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# rdkit RDKit is the cheminformatics toolkit: a molecule is a graph (`Chem.Mol`), written as SMILES, queried with SMARTS, given coordinates by distance geometry. Tools: `$RDKIT_PYTHON` (the pinned venv, with numpy and pandas), `harness_rdkit` on `PYTHONPATH` (build, embed, write, compare), `$RDKIT_TOOLCHAIN/verdict.py` (the pane header). Never install another RDKit. ## Build, write, verdict ```bash "$RDKIT_PYTHON" molecules/hello.py # runs the script → out/<name>.sdf and the rest "$RDKIT_PYTHON" "$RDKIT_TOOLCHAIN/verdict.py" # judges the newest molecule → pane header "$RDKIT_PYTHON" "$RDKIT_TOOLCHAIN/harness_rdkit.py" design "CCO" ethanol # the same, without a script ``` ```python from harness_rdkit import design, mol_from_smiles, embed_conformers, embed_3d, write_outputs, properties, similarity, substructure design("CC(C)Cc1ccc(cc1)C(C)C(=O)O", "ibuprofen") # parse → 10 conformers → minimise → describe → write design("CC(C)(O)Cc1ccc(C(C)C(=O)O)cc1", "ibuprofen_oh", parent="ibuprofen") # an analogue: name its parent mol = mol_from_smiles("CN1C=NC2=C1C(=O)N(C)C(=O)N2C", "caffeine") # the steps, when work happens between confs = embed_conformers(mol, n=10, seed=7) # ETKDGv3 + MMFF94, deduplicated, lowest first, aligned write_outputs(confs, "caffeine", parent=False) # False: not an analogue of anything here ``` What `design`/`write_outputs` write to `out/`, and what each is for: | file | what it is...

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Author
autonomous-ai
Repository
autonomous-ai/openharness
Created
1 months ago
Last Updated
today
Language
C
License
MIT

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