Metadata-Version: 2.1
Name: mhfp
Version: 1.6
Summary: Molecular MHFP fingerprints for cheminformatics applications
Home-page: https://github.com/reymond-group/mhfp
Author: Daniel Probst
License: UNKNOWN
Description: # MHFP
        
        MHFP6 (MinHash fingerprint, up to six bonds) is a molecular fingerprint which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. Furthermore, MHFP6 outperforms ECFP4 in approximate nearest neighbor searches by two orders of magnitude in terms of speed, while decreasing the error rate.
        
        ![Visual Abstract for MHFP](http://doc.gdb.tools/mhfp_ga.jpg)
        
        **Associated Publication**: https://chemrxiv.org/articles/A_Probabilistic_Molecular_Fingerprint_for_Big_Data_Settings/7176350
        
        ## Materials
        The performance of MHFP has been avaluated using [Open-source platform to benchmark fingerprints for ligand-based virtual screening](https://github.com/rdkit/benchmarking_platform) and [ChEMBL24](https://www.ebi.ac.uk/chembl/downloads). Python (pyspark) scripts for the ChEMBL24 benchmark can be found in this repository.
        
        ## Dependencies
        MHFP requires Python 3.x and Numpy.
        
        MHFP requires the cheminformatics library RDKit.
        
        ## Installation
        MHFP can be installed using the Python packet manager pip:
        ```bash
        pip install mhfp
        ```
        
        ## Usage
        Usage is straightforward:
        ```Python
        from mhfp.encoder import MHFPEncoder
        
        mhfp_encoder = MHFPEncoder()
        
        fp_a = mhfp_encoder.encode('CCOC1=C(C=C(C=C1)S(=O)(=O)N(C)C)C2=NC(=O)C3=C(N2)C(=NN3C)C(C)(C)C')
        fp_b = mhfp_encoder.encode('CCCC1=NN(C2=C1NC(=NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C')
        fp_c = mhfp_encoder.encode('O=C(OC)C(C1CCCCN1)C2=CC=CC=C2')
        
        print(MHFPEncoder.distance(fp_a, fp_a))
        print(MHFPEncoder.distance(fp_a, fp_b))
        print(MHFPEncoder.distance(fp_a, fp_c))
        print(MHFPEncoder.distance(fp_b, fp_c))
        
        #> 0.0
        #> 0.45849609375
        #> 0.97998046875
        #> 0.97216796875
        ```
        
        ### LSH Forest
        As the fingerprints are created using the locality sensitive hashing scheme MinHash, they are ready to be used with LSH-based algorithms:
        ```Python
        from mhfp.encoder import MHFPEncoder
        from mhfp.lsh_forest import LSHForestHelper
        
        mhfp_encoder = MHFPEncoder()
        
        fp_a = mhfp_encoder.encode('CCOC1=C(C=C(C=C1)S(=O)(=O)N(C)C)C2=NC(=O)C3=C(N2)C(=NN3C)C(C)(C)C')
        fp_b = mhfp_encoder.encode('CCCC1=NN(C2=C1NC(=NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C')
        fp_c = mhfp_encoder.encode('O=C(OC)C(C1CCCCN1)C2=CC=CC=C2')
        fp_q = mhfp_encoder.encode('COC1=C(C=C(C=C1)S(=O)(O)N(C)C)C2=NC(=O)C3=C(N2)C(=NN3C)C(C)(C)C')
        
        lsh_forest_helper = LSHForestHelper()
        
        fps = [fp_a, fp_b, fp_c]
        
        for i, fp in enumerate(fps):
            lsh_forest_helper.add(i, fp)
        
        lsh_forest_helper.index()
        
        print(lsh_forest_helper.query(fp_q, 1, fps))
        
        #> [0]
        ```
        
        ### SECFP
        SECFP (SMILES Extended Connectifity Fingerprint) creation is achieved by calling the static method `MHFPEncoder.secfp_from_smiles()`:
        ```Python
        from mhfp.encoder import MHFPEncoder
        fp = MHFPEncoder.secfp_from_smiles('CCOC1=C(C=C(C=C1)S(=O)(=O)N(C)C)C2=NC(=O)C3=C(N2)C(=NN3C)C(C)(C)C')
        ```
        
        
        ## Documentation
        ### MHFPEncoder
        The class for encoding SMILES and RDKit molecule instances as MHFP fingerprints.
        
        ```Python
        __init__(n_permutations = 2048, seed = 42)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | n_permutations | ```2048``` | Analogous to the number of bits ECFP fingerprints are folded into. Higher is better, lower is less exact. |
        | seed | ```42``` | The seed for the MinHash operation. Has to be the same for comparable fingerprints. |
        
        ```Python
        MHFPEncoder.encode(in_smiles, radius = 3, rings = True, kekulize = True, sanitize = True)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | in_smiles | | The input SMILES. |
        | radius | ```3``` | Analogous to the radius for the Morgan fingerprint. The default radius 3 encodes SMILES to MHFP6. |
        | rings | ```True``` | Whether rings in the molecule are included in the fingerprint. As a radii of 3 fails to encode rings and there is no way to determine ring-membership in a substructure SMILES, this considerably increases performance. |
        | kekulize | ```True```  | Whether or not to kekulize the molecule before extracting substructure SMILES. |
        | sanitize | ```True```  | Whether or not to sanitize the molecule (using RDKit) before extracting substructure SMILES. |
        
        **Returns** a `numpy.ndarray` containing the fingerprint hashes.
        
        
        ```Python
        MHFPEncoder.encode_mol(in_mol, radius = 3, rings = True, kekulize = True)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | in_mol | | The input RDKit molecule instance. |
        | radius | ```3``` | Analogous to the radius for the Morgan fingerprint. The default radius 3 encodes SMILES to MHFP6. |
        | rings | ```True``` | Whether rings in the molecule are included in the fingerprint. As a radii of 3 fails to encode rings and there is no way to determine ring-membership in a substructure SMILES, this considerably increases performance. |
        | kekulize | ```True```  | Whether or not to kekulize the molecule before extracting substructure SMILES. |
        
        **Returns** a `numpy.ndarray` containing the fingerprint hashes.
        
        ```Python
        MHFPEncoder.distance(a, b)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | a | | A `numpy.ndarray` containing fingerprint hashes. |
        | b | | A `numpy.ndarray` containing fingerprint hashes. |
        
        **Returns** a `float` representing the distance between two MHFP encoded molecules.
        
        ```Python
        MHFPEncoder.secfp_from_mol(in_mol, length=2048, radius=3, rings=True, kekulize=True)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | in_mol | | The input RDKit molecule instance. |
        | length | ```2048``` | The length of the folded fingerprint. |
        | radius | ```3``` | Analogous to the radius for the Morgan fingerprint. The default radius 3 encodes SMILES to MHFP6. |
        | rings | ```True``` | Whether rings in the molecule are included in the fingerprint. As a radii of 3 fails to encode rings and there is no way to determine ring-membership in a substructure SMILES, this considerably increases performance. |
        | kekulize | ```True```  | Whether or not to kekulize the molecule before extracting substructure SMILES. |
        
        **Returns** a `numpy.ndarray` containing the fingerprint values.
        
        ```Python
        MHFPEncoder.secfp_from_smiles(in_mol, length=2048, radius=3, rings=True, kekulize=True, sanitize=False)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | in_mol | | The input RDKit molecule instance. |
        | length | ```2048``` | The length of the folded fingerprint. |
        | radius | ```3``` | Analogous to the radius for the Morgan fingerprint. The default radius 3 encodes SMILES to MHFP6. |
        | rings | ```True``` | Whether rings in the molecule are included in the fingerprint. As a radii of 3 fails to encode rings and there is no way to determine ring-membership in a substructure SMILES, this considerably increases performance. |
        | kekulize | ```True```  | Whether or not to kekulize the molecule before extracting substructure SMILES. |
        | sanitize | ```False``` | Whether or not to sanitize the SMILES when parsing it using RDKit. |
        
        **Returns** a `numpy.ndarray` containing the fingerprint values.
        
        ### LSHForestHelper
        If you know your way around Python, I suggest you use the ```LSHForest``` class directly. See code for details.
        
        ```Python
        __init__(dims = 2048, n_prefix_trees = 64)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | dims | ```2048``` | **Has to be equal to the number of permutations** `n_permutations` **set when initializing** `MHFPEncoder` |
        | n_prefix_trees | ```64``` | The number of prefix trees. Lower number results in lower memory usage but less quality |
        
        ```Python
        LSHForestHelper.add(key, mhfp):
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | key | | The key that will be retrieved by a query. It's recommended to use integers. |
        | mhfp | | The `numpy.ndarray` containing the fingerprint hashes. |
        
        ```Python
        LSHForestHelper.index():
        ```
        **Has to be run after adding entities and before running queries!**
        
        ```Python
        MHFPEncoder.query(query_mhfp, k, data, kc=10)
        ```
        | Parameter | Default | Description |
        |---|---|---|
        | query_mhfp | | A `numpy.ndarray` containing fingerprint hashes. |
        | k | | The number of nearest neighbors to be retrieved. |
        | data | | Data mapping keys to fingerprints. See LSH Forest example for details. |
        | kc | 10 | Initially k is multiplied by kc and k &middot; kc (ANN) results are returned. The top k are then selected from these k &middot; kc using linear scan. |
        
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