Python DataStructs.BulkTanimotoSimilarity方法代码示例

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def doSimSearch(model_name):
	if os.name == 'nt': sep = '\\'
	else: sep = '/'
	mod = model_name.split(sep)[-1].split('.')[0]
	try:
		with zipfile.ZipFile(os.path.dirname(os.path.abspath(__file__)) + sep + 'actives' + sep + mod + '.smi.zip', 'r') as zfile:
			comps = [i.split('\t') for i in zfile.open(mod + '.smi', 'r').read().splitlines()]
	except IOError: return
	comps2 = []
	afp = []
	for comp in comps:
		try:
			afp.append(calcFingerprints(comp[1]))
			comps2.append(comp)
		except: pass
	ret = []
	for i,fp in enumerate(querymatrix):
		sims = DataStructs.BulkTanimotoSimilarity(fp,afp)
		idx = sims.index(max(sims))
		ret.append([sims[idx], mod] + comps2[idx] + [smiles[i]])
	return ret

#prediction runner 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def calculate_internal_pairwise_similarities(smiles_list: Collection[str]) -> np.array:
    """
    Computes the pairwise similarities of the provided list of smiles against itself.

    Returns:
        Symmetric matrix of pairwise similarities. Diagonal is set to zero.
    """
    if len(smiles_list) > 10000:
        logger.warning(f'Calculating internal similarity on large set of '
                       f'SMILES strings ({len(smiles_list)})')

    mols = get_mols(smiles_list)
    fps = get_fingerprints(mols)
    nfps = len(fps)

    similarities = np.zeros((nfps, nfps))

    for i in range(1, nfps):
        sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i])
        similarities[i, :i] = sims
        similarities[:i, i] = sims

    return similarities 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def highest_tanimoto_precalc_fps(mol, fps):
    """

    Args:
        mol: Rdkit molecule
        fps: precalculated ECFP4 bitvectors

    Returns:

    """

    if fps is None or len(fps) == 0:
        return 0

    fp1 = AllChem.GetMorganFingerprintAsBitVect(mol, 2, 4096)
    sims = np.array(DataStructs.BulkTanimotoSimilarity(fp1, fps))

    return sims.max() 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def doPercentileCalculation(model_name):
	global rdkit_mols
	#expensive to unzip training file - so only done if smiles requested
	if options.ad_smiles:
		smiles = get_training_smiles(model_name)
	ad_data = getAdData(model_name)
	def calcPercentile(rdkit_mol):
		sims = DataStructs.BulkTanimotoSimilarity(rdkit_mol,ad_data[:,0])
		bias = ad_data[:,2].astype(float)
		std_dev = ad_data[:,3].astype(float)
		scores = ad_data[:,5].astype(float)
		weights = sims / (bias * std_dev)
		critical_weight = weights.max()
		percentile = percentileofscore(scores,critical_weight)
		if options.ad_smiles:
			critical_smiles = smiles[np.argmax(weights)]
			result = percentile, critical_smiles
		else:
			result = percentile, None
		return result
	ret = [calcPercentile(x) for x in rdkit_mols]
	return model_name, ret

#prediction runner for percentile calculation 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def ClusterFps(fps, cutoff=0.2):
  # (ytz): this is directly copypasta'd from Greg Landrum's clustering example.
  dists = []
  nfps = len(fps)
  from rdkit import DataStructs
  for i in range(1, nfps):
    sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i])
    dists.extend([1 - x for x in sims])
  from rdkit.ML.Cluster import Butina
  cs = Butina.ClusterData(dists, nfps, cutoff, isDistData=True)
  return cs 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def ClusterFps(fps, cutoff=0.2):
  # (ytz): this is directly copypasta'd from Greg Landrum's clustering example.
  dists = []
  nfps = len(fps)
  for i in range(1, nfps):
    sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i])
    dists.extend([1 - x for x in sims])
  cs = Butina.ClusterData(dists, nfps, cutoff, isDistData=True)
  return cs 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def tanimoto_worker(k, fps):
    """Get per-fingerprint Tanimoto distance vector."""
    # pylint: disable=no-member
    sims = DataStructs.BulkTanimotoSimilarity(fps[k], fps[(k + 1):])
    dists_k = [1. - s for s in sims]
    return np.array(dists_k), 0 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def tanimoto_single(fp, fps):
    """Get per-fingerprint Tanimoto distance vector."""
    # pylint: disable=no-member
    sims = DataStructs.BulkTanimotoSimilarity(fp, fps)
    dists = [1. - s for s in sims]
    return np.array(dists), 0 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def __compute_diversity(mol, fps):
        ref_fps = Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect(mol, 4, nBits=2048)
        dist = DataStructs.BulkTanimotoSimilarity(ref_fps, fps, returnDistance=True)
        score = np.mean(dist)
        return score 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def calculate_pairwise_similarities(smiles_list1: List[str], smiles_list2: List[str]) -> np.array:
    """
    Computes the pairwise ECFP4 tanimoto similarity of the two smiles containers.

    Returns:
        Pairwise similarity matrix as np.array
    """
    if len(smiles_list1) > 10000 or len(smiles_list2) > 10000:
        logger.warning(f'Calculating similarity between large sets of '
                       f'SMILES strings ({len(smiles_list1)} x {len(smiles_list2)})')

    mols1 = get_mols(smiles_list1)
    fps1 = get_fingerprints(mols1)

    mols2 = get_mols(smiles_list2)
    fps2 = get_fingerprints(mols2)

    similarities = []

    for fp1 in fps1:
        sims = DataStructs.BulkTanimotoSimilarity(fp1, fps2)

        similarities.append(sims)

    similarities = np.array(similarities)

    return similarities 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def diversity(smile, fps):
    val = 0.0
    low_rand_dst = 0.9
    mean_div_dst = 0.945
    ref_mol = Chem.MolFromSmiles(smile)
    ref_fps = Chem.GetMorganFingerprintAsBitVect(ref_mol, 4, nBits=2048)
    dist = DataStructs.BulkTanimotoSimilarity(
        ref_fps, fps, returnDistance=True)
    mean_dist = np.mean(np.array(dist))
    val = remap(mean_dist, low_rand_dst, mean_div_dst)
    val = np.clip(val, 0.0, 1.0)
    return val

#============== 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def tanimoto_1d(fps):
    ds = []
    for i in range(1, len(fps)):
        ds.extend(DataStructs.BulkTanimotoSimilarity(
            fps[i], fps[:i], returnDistance=True))
    return ds 

# 需要导入模块: from rdkit import DataStructs [as 别名]
# 或者: from rdkit.DataStructs import BulkTanimotoSimilarity [as 别名]
def diversity(fake_path, real_path=None, is_active=False):
    """ Molecular diversity measurement based on Tanimoto-distance on ECFP6 fingerprints,
    including, intra-diversity and inter-diversity.

    Arguments:
        fake_path (str): the file path of molecules that need to measuring diversity

        real_path (str, optional): the file path of molecules as the reference, if it
            is provided, the inter-diversity will be calculated; otherwise, the intra-diversity
            will be calculated.
        is_active (bool, optional): selecting only active ligands (True) or all of the molecules (False)
            if it is true, the molecule with PCHEMBL_VALUE >= 6.5 or SCORE > 0.5 will be selected.
            (Default: False)

    Returns:
        df (DataFrame): the table that contains columns of CANONICAL_SMILES
            and diversity value for each molecules

    """
    fake = pd.read_table(fake_path)
    fake = fake[fake.SCORE > (0.5 if is_active else 0)]
    fake = fake.drop_duplicates(subset='CANONICAL_SMILES')
    fake_fps, real_fps = [], []
    for i, row in fake.iterrows():
        mol = Chem.MolFromSmiles(row.CANONICAL_SMILES)
        fake_fps.append(AllChem.GetMorganFingerprint(mol, 3))
    if real_path:
        real = pd.read_table(real_path)
        real = real[real.PCHEMBL_VALUE >= (6.5 if is_active else 0)]
        for i, row in real.iterrows():
            mol = Chem.MolFromSmiles(row.CANONICAL_SMILES)
            real_fps.append(AllChem.GetMorganFingerprint(mol, 3))
    else:
        real_fps = fake_fps
    method = np.min if real_path else np.mean
    dist = 1 - np.array([method(DataStructs.BulkTanimotoSimilarity(f, real_fps)) for f in fake_fps])
    fake['DIST'] = dist
    return fake