Scopy(Screening COmpounds), based on RDKit, is an open-source virtual screening tool. Its calculation is based on the various drug-likeness rules and many unexpected substructure presented in SMARTS format. Besides, Scopy could also compute some fingerprints that retrieved from structures
Drug-likeness rules are set of guidelines for the structural properties of compounds, used for fast calculation of drug-like properties of a molecule. These guidelines are not absolute, nor are they intended to form strict cutoff values for which property values are drug-like and which are not drug-like. Nevertheless, they can be quite effective and efficient.
In order to implement checking molecule under drug-likeness rules, physicochemical properties should be calculated firstly.
This moudle is used to calculated properise that contained in our collectded rules
---
up to now(2019.07.22), we have achived following properties:
Molcular Weight >>> MW
Number of bonds >>> nBond
Number of atoms >>> nAtom
Number of heteroatoms >>> nHet
Number of heavy atom >>> nHev
Number of rotable bonds >>> nRot
Number of rigid bonds >>> nRig
Number of SSSR >>> nRing
logP >>> logP
logD >>> logD
logSw >>> logSw
Acid or Base >>> ab
pKa >>> pKa
QED >>> qed
Molecular refraction >>> MR
Number of hydrogen bond donors >>> nHD
Number of hydrogen bond acceptors >>> nHA
Number of hydrogen bond donors& acceptors >>> nHB
Aromatic proportion >>> AP
sp3 hybridized carbons/total carbon count >>> Fsp3
TPSA >>> TPSA
Number of atoms involved in the biggest system ring >>> MaxRing
Number of Sterocenterss >>> nStero
HetCarbonRatio >>> HetRatio
synthetic accessibility score >>> SAscore
natural product-likeness score >>> NPscore
Number of single bonds >>> nSingle
Number of double bobds >>> nDoudle
Number of triple bonds >>> nTriple
Volume of mol >>> Vol
Density >>> Dense
MolFCharge >>> fChar
Number of Carbon atoms >>> nC
Number of Boron atoms >>> nB
Number of Chlorin atoms >>> nCl
Number of Bromine atoms >>> nBr
Number of Iodine atoms >>> nI
Number of Phosphor atoms >>> P
Number of Sulfur atoms >>> nS
Number of Oxygen atoms >>> nO
Number of Nitrogen atoms >>> nN
---
Followed should be achieved in the future:
Formal total charge of the compound
Number of Charged Groups
This function is implemented in module scopy.druglikeness
>>> from scopy.druglikeness import molproperty
>>> from rdkit import Chem>>> mol = Chem.MolFromSmiles('C1=CC=CC1')
>>> res = molproperty.GetProperties(mol)
>>> print(res)
Properties(MW=66.1, Vol=81.21, Dense=0.81, fChar=0, nBond=5, nAtom=11, nCarbon=5, nHD=0, nHA=0, nHB=0, nHet=0, nStero=0, nHev=5, nRot=0, nRig=5, nRing=1, logP=1.5, logD=1.2081988287461527, pKa=-7.418658028559937, logSw=-1.21, ab='base', MR=22.9, tPSA=0.0, AP=0.0, HetRatio=0.0, Fsp3=0.2, MaxRing=5, QED=0.4, SAscore=3.31, NPscore=2.16, nSingle=3, nDouble=2, nTriple=0, nC=5, nB=0, nF=0, nCl=0, nBr=0, nI=0, nP=0, nS=0, nO=0, nN=0)You could also compute each property respectively, like:
>>> mol = Chem.MolFromSmiles('C1=CC=CC2C=CC3C4C=CC=CC=4C=CC=3C1=2')
>>> res_1 = molproperty.CalculateLogP(mol)
>>> res_2 = molproperty.CalculateNumHAcceptors(mol)
>>> res_3 = molproperty.CalculateNumRing(mol)
>>> print(res_1,res_2,res_3)
5.15 0 4up to now(2019.07.09), we have achived following rules:
Egan Rule 0<=tPSA<=132; -1<=logP<=6
Veber Rule nRot<= 10; tPSA<= 140; nHB<= 12
LipinskiRule MW<=500; logP<=5, nHD<=5, nHA <=10
BeyondRo5 Mw<=1000; -2<=logP<=10; nHD<=6, nHA<=15; tPSA<=250; nRot<=20
Pfizer Rule logP>3; PSA<75
GSK Rule MW<=400; logP<=4
OralMacrocycles MW<1000; logP<10; nHD<5; PSA<250
Oprea Rule nRing>=3,nRig>=18,nRot>=6
Ghose Rule -0.4<logP<5.6; 160<MW<480; 40<MR<130; 20<nAtom<70
Xu Rule nHD<=5; nHA <= 10; 3 <= rot <= 35; 1 <= nring <= 7; 10 <= nhev <= 50
Ro4 Rule MW<=400; logP<=4; nHD<=4; NHA<=8; PSA<=120
Ro3 Rule MW<=300; -3<=logP<=3; nHD<=3; nHA<=6; PSA<=60
Ro2 Rule MW<=200; logP<=2; nHD<=2; nHA<=4
---
Followed should be achieved in the future:
OpreaTwo Rule
Kelder Rule
REOS Rule
GoldenTriangle
Schneider Rule
DrugLikeOne
DrugLikeTwo
Zinc
Cns
Respiratory
this function is realized in module scopy.druglikeness
>>> from scopy.druglikeness import rulesfilter
>>> from rdkit import Chem
>>> mol = Chem.MolFromSmiles('C1=CC=CC2C=CC3C4C=CC=CC=4C=CC=3C1=2')
>>> res = rulesfilter.CheckLipinskiRule(mol)
>>> print(res)
LipinskiRule(Disposed='Accepted', nViolate=1)The filed 'Disposed' is meant molecular state after rule applied. 'Accepted' means obey the rule. attribute 'nViolated' means the number of violated requirement of a rule.
If you want to get specific properties suggested in rule, you could pass the True to Parameter 'detail'(default: False)
>>> res = rulesfilter.CheckLipinskiRule(mol,detail=True)
>>> print(res)
LipinskiRule(MW=228.29, logP=5.15, nHD=0, nHA=0, Disposed='Accept', nViolated=1)Customized rules are also supported
>>> res = rulesfilter.Check_CustomizeRule(mol,prop_kws={'MW':(None,500),
'nRing':(1,5),
'nHB':(1,5)},
detail=True,closed_interval=False)
>>> print(res)
CustomizeRule(MW=228.29, nRing=4, nHB=0, nViolate=1, VioProp=['nHB'])Some rules could be visualized
up to now(2019.07.20), following rules could be visualized:
BeyondRo5 rule
Lipinski rule
Xu'rule
Pfizer's rule
>>> from scopy.druglikeness import visualize
>>> mol = Chem.MolFromSmiles('Fc1ccc(CC2=NNC(=O)c3ccccc23)cc1C(=O)N4CCc5cccc6C(=O)NCC4c56')>>> visualize.VisualizeXu(mol)
>>> visualize.VisualizeLipinski(mol)
>>> visualize.VisualizeBeyondRo5(mol)
radar plot, the compounds values are materialized by the blue line, which should fall within the optimal pale blue.
>>> visualize.PfizerPositioning(mol)
The filters consist of a series of molecular query strings written using the SMARTS coding language described by Daylight.
"""
---
Up to now(2019.07.02), we have collected followed endpoints(the number of SMARTS):
Acute_Aquatic_Toxicity(99)
AlphaScreen_FHs(6)
AlphaScreen_GST_FHs(34)
AlphaScreen_HIS_FHs(19)
Biodegradable(9)
BMS(180)
Chelating(55)
Developmental_Mitochondrial(12)
Genotoxic_Carcinogenicity_Mutagenicity(117)
Idiosyncratic(35)
LD50_oral(20)
Luciferase_Inhibitory(3)
NonBiodegradable(19)
NonGenotoxic_Carcinogenicity(23)
NTD(105)
Pains(480)
Potential_Electrophilic(119)
Promiscuity(177)
Reactive_Unstable_Toxic(335)
Skin_Sensitization(155)
SureChEMBL(165)
---
Total: 23 endpints with 2167 SMARTS
"""This function is implemented in module scopy.structure_alert
>>> from scopy.structure_alert import FliterWithSmarts
>>> smis = [
'C1=CC=C2C(=O)CC(=O)C2=C1', #Pains
'C1=CC=CC(COCO)=C1', #Potential_Electrophilic
'N1=NC=CN1C=O', #Promiscuity
'CC(=O)OC(=O)C1C=COC1', #Skin_Sensitization
'S',
'CCCCC(=O)[H]', #Biodegradable
'C1=CN=C(C(=O)O)C=C1', #Chelating
'C(OC)1=CC=C2OCC3OC4C=C(OC)C=CC=4C(=O)C3C2=C1',
'C1=C2N=CC=NC2=C2N=CNC2=C1', #Genotoxic_Carcinogenicity_Mutagenicity
'N(CC)(CCCCC)C(=S)N', #Idiosyncratic
]
>>> mols = (Chem.MolFromSmiles(x) for x in smis)
>>> res = FliterWithSmarts.Check_PAINS(mols=mols)
>>> res
[CheckRes(Disposed=False, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains'),
CheckRes(Disposed=True, Endpoint='Pains')]parametermols if Iterable object(like .sdf), each element is a rdkit.Chem.rdchem.Mol
The filed 'Disposed' is meant molecular state after filter applied. True meant unmatched any SMARTS in the specific endpoint. For False, at least one SMARTS to be matched.
Similarly, you could pass True to parameter detail show to get more information.
>>> mols = (Chem.MolFromSmiles(x) for x in smis)
>>> res = FliterWithSmarts.Check_PAINS(mols=mols, detail=True)
>>> res
[CheckRes(Disposed=False, MatchedAtoms=[((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10),)], MatchedNames=['Keto_keto_beta_A'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains'),
CheckRes(Disposed=True, MatchedAtoms=['-'], MatchedNames=['-'], Endpoint='Pains')]"MatchedAtoms" shown which atoms has matched SMARTS. "MatchedNames" is the name or code of SMARTS. You could find it in the folder */Scopy/data/SMARTS
If you get the indices of atoms, you could highlight them
>>> mol = Chem.MolFromSmiles('C1=CC=C2C(=O)CC(=O)C2=C1')
>>> pic = FliterWithSmarts.VisualizeFragment(mol,(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
>>> pic