Closed Sulstice closed 4 months ago
class MixedMartialArts(object):
def __init__(self):
self.name = 'mixed_martial_arts'
@staticmethod
def get_smiles():
smiles = {
"1-androstenedione" :"C[C@]12CC[C@H]3[C@H]([C@@H]1CCC2=O)CC[C@@H]4[C@@]3(C=CC(=O)C4)C",
"dextroamphetamine" :"C[C@@H](CC1=CC=CC=C1)N",
"anastrozole" :"CC(C)(C#N)C1=CC(=CC(=C1)CN2C=NC=N2)C(C)(C)C#N",
"arimistane" : "C[C@]12CC[C@H]3[C@H]([C@@H]1CCC2=O)C(=O)C=C4[C@@]3(CCC=C4)C",
"boldenone" :"C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@@H]2O)CCC4=CC(=O)C=C[C@]34C",
"cardarine" : "CC1=C(C=CC(=C1)SCC2=C(N=C(S2)C3=CC=C(C=C3)C(F)(F)F)C)OCC(=O)O",
"clenbuterol" :"CC(C)(C)NCC(C1=CC(=C(C(=C1)Cl)N)Cl)O",
"clomiphene" : "CCN(CC)CCOC1=CC=C(C=C1)/C(=C(\C2=CC=CC=C2)/Cl)/C3=CC=CC=C3",
"cocaine" : "CN1[C@H]2CC[C@@H]1[C@H]([C@H](C2)OC(=O)C3=CC=CC=C3)C(=O)OC",
"dehydroepiandrosterone":"C[C@]12CC[C@H]3[C@H]([C@@H]1CCC2=O)CC=C4[C@@]3(CC[C@@H](C4)O)C",
"dehydrochlormethyltestosterone" : "C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C)O)CCC4=C(C(=O)C=C[C@]34C)Cl",
"furosemide" : "C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl",
"hydrochlorothiazide" : "C1NC2=CC(=C(C=C2S(=O)(=O)N1)S(=O)(=O)N)Cl",
"4-amino-6-chloro-1,3-benzenedisulfonamide" : "C1=C(C(=CC(=C1Cl)S(=O)(=O)N)S(=O)(=O)N)N",
"drostanolone" :"C[C@@H]1C[C@]2([C@@H](CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC[C@@H]4O)C)CC1=O)C",
"erythropoietin" : "[H]C1=C([H])C(C2=C([H])C([H])=C([H])C([H])=C2C3=NN=NC3)=C([H])C([H])=C1C(N4C(C([H])([H])O)=C(Cl)N=C4C([H])([H])C(C([H])([H])C([H])(C)[H])([H])[H])([H])[H]",
"GHRP-6":"C[C@@H](C(=O)N[C@@H](CC1=CNC2=CC=CC=C21)C(=O)N[C@H](CC3=CC=CC=C3)C(=O)N[C@@H](CCCCN)C(=O)N)NC(=O)[C@@H](CC4=CNC5=CC=CC=C54)NC(=O)[C@H](CC6=CN=CN6)N",
"ghrp-2":"C[C@H](C(=O)N[C@H](CC1=CC2=CC=CC=C2C=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CC3=CNC4=CC=CC=C43)C(=O)N[C@H](CC5=CC=CC=C5)C(=O)N[C@@H](CCCCN)C(=O)N)N",
"higenamine":"C1CNC(C2=CC(=C(C=C21)O)O)CC3=CC=C(C=C3)O",
"ibutamoren":"CC(C)(C(=O)N[C@H](COCC1=CC=CC=C1)C(=O)N2CCC3(CC2)CN(C4=CC=CC=C34)S(=O)(=O)C)N",
"insulin-like Growth Factor-1 (IGF-1) Protein": "",
"ipamorelin": "CC(C)(C(=O)N[C@@H](CC1=CN=CN1)C(=O)N[C@H](CC2=CC3=CC=CC=C3C=C2)C(=O)N[C@H](CC4=CC=CC=C4)C(=O)N[C@@H](CCCCN)C(=O)N)N",
"ligandrol" : "C1C[C@@H](N(C1)C2=CC(=C(C=C2)C#N)C(F)(F)F)[C@H](C(F)(F)F)O",
"meldonium":"C[N+](C)(C)NCCC(=O)[O-]",
"methyltestosterone":"C[C@]12CCC(=O)C=C1CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC[C@]4(C)O)C",
"mesterolone" : "C[C@H]1CC(=O)C[C@H]2[C@]1([C@H]3CC[C@]4([C@H]([C@@H]3CC2)CC[C@@H]4O)C)C",
"methandienone" : "C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C)O)CCC4=CC(=O)C=C[C@]34C",
"modafinil" : "C1=CC=C(C=C1)C(C2=CC=CC=C2)S(=O)CC(=O)N",
"nandrolone" : "C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@@H]2O)CCC4=CC(=O)CC[C@H]34",
"19-norandrosterone":"C[C@]12CC[C@@H]3[C@H]4CC[C@H](C[C@@H]4CC[C@H]3[C@@H]1CCC2=O)O",
"ostarine":"C[C@](COC1=CC=C(C=C1)C#N)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O",
"ozone":"[O-][O+]=O",
"stanozolol":"C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C)O)CC[C@@H]4[C@@]3(CC5=C(C4)NN=C5)C",
"tamoxifen":"CC/C(=C(\C1=CC=CC=C1)/C2=CC=C(C=C2)OCCN(C)C)/C3=CC=CC=C3",
"exogenous testosterone":"C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@@H]2O)CCC4=CC(=O)CC[C@]34C",
"tetrahidrocannabinol":"CC(O1)(C)[C@@]2(C)[C@](C=C(C)CC2)(C)C3=C1C=C(CCCCC)C=C3O",
"trenbolone":"C[C@]12C=CC3=C4CCC(=O)C=C4CC[C@H]3[C@@H]1CC[C@@H]2O"
}
return smiles
@staticmethod
def get_smarts():
smarts = {}
return smarts
Our target similarity is to see if the query: Erythropoietin matches any popular drugs for MMA fighting. The compound in question looks like this:
Performing a Tanimoto Search
for key, value in mma_compounds.items():
# Most Popular for MMA Fighting.
if key == 'Erythropoietin':
criteria = 0.05
fingerprint = convert_to_fingerprint(value)
print (value)
tanimoto_scores = DataStructs.BulkTanimotoSimilarity(fingerprint, cannabis_reference_fingerprints)
for i, value in enumerate(tanimoto_scores):
if value > 0.21:
print ("Matches: %s | %s " % (cannabis_phytochemicals_names[i], cannabis_phytochemicals[i]))
if all(x > criteria for x in tanimoto_scores):
print ('Tanimoto Accepted: %s : %s' % (key, value))
We can evaluate whether something could be similar and share the same protein targets? The output:
Matches: cannabioxepane | CCCCCC1=CC2=C3C(=C1)OCC(=C)C4=C3C(=C(C=C4)C)O2
Matches: 8-hydroxycannabinol | CCCCCC1=CC(=C2C(=C1)OC(C3=C2C=C(C(O)=C3)C)(C)C)O
Matches: cannabimovone | CCCCCC1=CC(=C(C(=C1)O)C2C(CC(C2O)C(=O)C)C(=C)C)O
Matches: anhydrocannabimovone | CCCCCC1=CC(=C2C(=C1)OC3C2C(CC3C(=O)C)C(=C)C)O
Let's start here to do some phenology mapping.
For the next steps:
performance_enhancement_drugs.py
__init__
https://www.mdpi.com/2227-9717/10/12/2734