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02 Calculators

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Levente Nagy edited this page Sep 22, 2026 · 3 revisions

Calculating physico-chemical properties

Currently available calculations: https://apidocs.chemaxon.com/python_api/apidocs/chemaxon/calculations.html

from chemaxon.io import import_mol
from chemaxon.calculations import logp, logd, pka, hlb

mol = import_mol('CC(=O)NC1=CC=C(O)C=C1') # paracetamol

print('logP:        ', logp(mol))
print('logD[pH 9.0]:', logd(mol, ph=9.0))
print('pKa:         ', pka(mol))
print('hlb:         ', hlb(mol))
logP:         0.92
logD[pH 9.0]: 0.8
pKa:          [atom: 3,  pka: 16.65 (ACIDIC)], [atom: 8,  pka: 9.46 (ACIDIC)], [atom: 2,  pka: -4.36 (BASIC)], [atom: 8,  pka: -5.94 (BASIC)]
hlb:          14.14

from chemaxon.io import import_mol
from chemaxon.calculations import pka

mol = import_mol('aspirin')

pka_result = pka(mol)
pka_result.mol

svg

Visualizing atom-index-based results

Many calculations return a value for every atom (e.g. pka_result.pka_values above). Function visualize_atom_values renders such per-atom results as an SVG image placing each value next to its atom. Required parameters are the molecule and the result list. The result list should consist of objects containing an atom_index field and one or more fields containing int or double results. Since pka_values isn't rounded, it can be rounded by setting the precision parameter (default value is 2).

from IPython.display import SVG
from chemaxon.io import visualize_atom_values
from chemaxon.calculations import AtomDoubleValue

SVG(visualize_atom_values(mol,  pka_result.pka_values, precision=2))

svg

When the result list carries more than one value per atom (e.g. ChargeValue has both formal_charge and total_charge), the value_attribute parameter should be used to pick the one to display:

from chemaxon.calculations import charge_by_atoms, ChargeValue

charge_result = charge_by_atoms(mol)
SVG(visualize_atom_values(mol, charge_result.charge_values, value_attribute='total_charge', precision=3))

svg

Visualized result can be exported to svg file as well using standard python functions:

svg_content = visualize_atom_values(mol, charge_result.charge_values, value_attribute='total_charge', precision=3)
with open("aspirinCharge.svg", "w") as f: f.write(svg_content)
import sys

!{sys.executable} -m pip install matplotlib
from chemaxon.calculations import logd_ph_range, PhRange
import matplotlib.pyplot as pyplt

res = logd_ph_range(import_mol('aspirin'), PhRange(0, 14, 0.5))
pyplt.plot([r.ph for r in res],[r.logd for r in res])
pyplt.title('logD by pH')
pyplt.xlabel('pH')
pyplt.ylabel('logD')
Text(0, 0.5, 'logD')

png

Chemical Terms

Please note that Chemaxon Python API does not support all available Chemical Terms functions. Check the documentation for details.

from chemaxon.calculations import evaluate

print('Formula:   ', evaluate(mol, 'formula()'))
print('Atom count:', evaluate(mol, 'atomCount()'))
print('Ring count:', evaluate(mol, 'ringCount()'))
print('Fsp3:      ', evaluate(mol, 'fsp3()'))
print('logP:      ', evaluate(mol, 'logP()'))
print('logS:      ', evaluate(mol, 'logS()'))
Formula:    C9H8O4
Atom count: 21
Ring count: 1
Fsp3:       0.1111
logP:       1.2548
logS:       -1.8489

Tautomer region of warfarin

warfarin = import_mol('warfarin')
import_mol(evaluate(warfarin, 'molFormat(genericTautomer(), "smarts")'))

svg

Calculating properties for a set of molecules

import sys

!{sys.executable} -m pip install pandas matplotlib
import pandas

mols = pandas.read_table('../nci1000.smiles', names=['SMILES', 'NCI_ID'])
mols
SMILES NCI_ID
0 CC1=CC(=O)C=CC1=O 1
1 S(SC1=NC2=CC=CC=C2S1)C3=NC4=C(S3)C=CC=C4 2
2 OC1=C(Cl)C=C(C=C1[N+]([O-])=O)[N+]([O-])=O 3
3 [O-][N+](=O)C1=CNC(=N)S1 4
4 NC1=CC2=C(C=C1)C(=O)C3=C(C=CC=C3)C2=O 5
... ... ...
995 OC(=O)CN(CC(O)=O)CC1=CC=CC=C1 1003
996 CC(C)N(CC(O)=O)CC(O)=O 1004
997 C1=CC=C(C=C1)C=NC(N=CC2=CC=CC=C2)C3=CC=CC=C3 1005
998 CC(C)(C)C[C]1(C)NC(=O)NC1=O 1006
999 CCOC(=O)C(C(C)C)C(=O)OCC 1007

1000 rows × 2 columns

mols['LOGP'] = mols.apply(lambda row: round(logp(import_mol(row['SMILES'])), 2), axis = 'columns')
mols['LOGD[3.0]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), ph=3.0), axis = 'columns')
mols['LOGD[7.4]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), ph=7.4), axis = 'columns')
mols['LOGD[11.0]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), ph=11.0), axis = 'columns')
mols
SMILES NCI_ID LOGP LOGD[3.0] LOGD[7.4] LOGD[11.0]
0 CC1=CC(=O)C=CC1=O 1 1.24 1.24 1.24 1.24
1 S(SC1=NC2=CC=CC=C2S1)C3=NC4=C(S3)C=CC=C4 2 6.22 6.22 6.22 6.22
2 OC1=C(Cl)C=C(C=C1[N+]([O-])=O)[N+]([O-])=O 3 2.15 1.96 0.25 0.25
3 [O-][N+](=O)C1=CNC(=N)S1 4 -0.22 -1.69 -0.27 -0.33
4 NC1=CC2=C(C=C1)C(=O)C3=C(C=CC=C3)C2=O 5 2.09 2.04 2.09 2.09
... ... ... ... ... ... ...
995 OC(=O)CN(CC(O)=O)CC1=CC=CC=C1 1003 -2.08 -2.08 -5.27 -6.17
996 CC(C)N(CC(O)=O)CC(O)=O 1004 -3.16 -3.92 -6.30 -6.31
997 C1=CC=C(C=C1)C=NC(N=CC2=CC=CC=C2)C3=CC=CC=C3 1005 5.72 5.72 5.72 5.72
998 CC(C)(C)C[C]1(C)NC(=O)NC1=O 1006 1.19 1.19 1.19 0.04
999 CCOC(=O)C(C(C)C)C(=O)OCC 1007 2.02 2.02 2.02 2.02

1000 rows × 6 columns

mols.hist(['LOGP', 'LOGD[3.0]', 'LOGD[7.4]', 'LOGD[11.0]'], bins=50)
array([[<Axes: title={'center': 'LOGP'}>,
        <Axes: title={'center': 'LOGD[3.0]'}>],
       [<Axes: title={'center': 'LOGD[7.4]'}>,
        <Axes: title={'center': 'LOGD[11.0]'}>]], dtype=object)

png

ADMET predictors

There are four endpoints available in the Chemaxon Python API falling into the ADMET predictions category: herg_classifiaction, herg_activity, bbb (Blood-Brain Barrier) and cns_mpo (CNS Multiparameter Optimisation).

from chemaxon.calculations import herg_classification, herg_activity, bbb, cns_mpo

print(str(mol), ' (aspirin)')
print()
print('HERG classification:', 'SAFE' if herg_classification(mol).classification == 0 else 'TOXIC')
print('HERG activity:      ', herg_activity(mol).value)
print('BBB score:          ', bbb(mol).score)
print('CNS MPO score:      ', cns_mpo(mol).score)
CC(=O)OC1=CC=CC=C1C(O)=O |c:6,8,t:4|  (aspirin)

HERG classification: SAFE
HERG activity:       3.788064026672559
BBB score:           4.050916887199017
CNS MPO score:       5.75

mols_admet = pandas.read_table('../nci50.smiles', names=['SMILES', 'NCI_ID'])

mols_admet['HERG classification'] = mols_admet.apply(lambda row: 'SAFE' if herg_classification(import_mol(row['SMILES'])).classification == 0 else 'TOXIC', axis = 'columns')
mols_admet['HERG activity'] = mols_admet.apply(lambda row: herg_activity(import_mol(row['SMILES'])).value, axis = 'columns')
mols_admet['BBB score'] = mols_admet.apply(lambda row: bbb(import_mol(row['SMILES'])).score, axis = 'columns')
mols_admet['CNS MPO score'] = mols_admet.apply(lambda row: cns_mpo(import_mol(row['SMILES'])).score, axis = 'columns')

mols_admet
SMILES NCI_ID HERG classification HERG activity BBB score CNS MPO score
0 CC1=CC(=O)C=CC1=O 1 SAFE 3.527087 4.402743 5.707000
1 S(SC1=NC2=CC=CC=C2S1)C3=NC4=C(S3)C=CC=C4 2 SAFE 5.288656 4.511565 3.289000
2 OC1=C(Cl)C=C(C=C1[N+]([O-])=O)[N+]([O-])=O 3 SAFE 3.994468 2.900463 5.199667
3 [O-][N+](=O)C1=CNC(=N)S1 4 SAFE 3.931435 2.691684 5.500000
4 NC1=CC2=C(C=C1)C(=O)C3=C(C=CC=C3)C2=O 5 SAFE 4.450256 5.024654 5.455111
5 OC(=O)C1=C(C=CC=C1)C2=C3C=CC(=O)C(=C3OC4=C2C=C... 6 SAFE 4.978091 3.096815 3.817072
6 CN(C)C1=C(Cl)C(=O)C2=C(C=CC=C2)C1=O 7 SAFE 3.972201 5.246618 5.869000
7 CC1=C(C2=C(C=C1)C(=O)C3=CC=CC=C3C2=O)[N+]([O-])=O 8 SAFE 4.716067 4.873092 5.111222
8 CC(=NO)C(C)=NO 9 SAFE 3.536242 2.390943 5.500000
9 C1=CC=C(C=C1)P(C2=CC=CC=C2)C3=CC=CC=C3 10 SAFE 5.191467 2.189764 3.000000
10 CC(C)(C)C1=C(O)C=C(C(=C1)O)C(C)(C)C 11 SAFE 4.976892 5.092122 3.721886
11 CC1=NN(C(=O)C1)C2=CC=CC=C2 12 SAFE 3.816882 5.105759 5.633500
12 NC1=CC=NC2=C1C=CC(=C2)Cl 13 TOXIC 5.067218 5.201635 5.445500
13 CCCCCC[CH]1CCCCN1 14 TOXIC 5.536671 4.765026 3.512570
14 O=CC1=C2C=CC=CC2=CC3=C1C=CC=C3 15 SAFE 4.847016 4.703717 3.835298
15 BrN1C(=O)CCC1=O 16 SAFE 3.435039 4.177664 5.869000
16 CCCCCCCCCCCCCCCC1=C(N)C=CC(=C1)O 17 SAFE 5.842166 5.126565 3.250000
17 C(COC1=C(C=CC=C1)C2=CC=CC=C2)OC3=CC=CC=C3C4=CC... 18 SAFE 5.455359 4.398490 2.953857
18 CCCCSCC 19 SAFE 4.269238 1.193937 4.618301
19 CC(=O)NC1=NC2=C(C=C1)C(=CC=N2)O 20 SAFE 4.032379 3.937892 5.500000
20 CC1=C2C=CC(=NC2=NC(=C1)O)N 21 SAFE 4.405076 3.333697 5.250000
21 CCOC(=O)C1=CN=C2N=C(N)C=CC2=C1O 22 SAFE 4.465663 3.077788 4.972333
22 CC1=CC(=NC=C1)N=CC2=CC=CC=C2 23 SAFE 4.700271 5.441334 4.008120
23 C[N+](C)(C)CC1=CC=CC=C1 24 SAFE 4.090068 2.036802 5.000000
24 C[N+](C)(C)C(=O)C1=CC=CC=C1 25 SAFE 4.083656 4.751954 5.000000
25 ICCC(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 26 SAFE 5.345193 2.174889 2.726521
26 CC1=CC(=C(C[N+](C)(C)C)C(=C1)C)C 27 SAFE 4.031780 2.203641 5.000000
27 C[C](O)(CC(O)=O)C1=CC=C(C=C1)[N+]([O-])=O 28 SAFE 4.012150 2.465327 5.144333
28 CC1=CC=C(C=C1)C(=O)C2=CC=C(Cl)C=C2 29 SAFE 5.042161 4.968342 3.216634
29 ON=CC1=CC=C(O)C=C1 30 SAFE 4.004053 3.737411 5.500000
30 CC1=CC(=C(N)C(=C1)C)C 31 SAFE 4.636959 5.064146 4.434087
31 CC1=CC=C(C=C1)C(=O)C2=CC=C(C=C2)[N+]([O-])=O 32 SAFE 4.811931 5.146606 4.597329
32 CC(O)(C1=CC=CC=C1)C2=CC=CC=C2 33 SAFE 4.433934 5.508138 4.112840
33 ON=CC1=CC(=CC=C1)[N+]([O-])=O 34 SAFE 3.767110 3.604711 5.750000
34 OC1=C2C=CC(=CC2=NC=C1[N+]([O-])=O)Cl 35 SAFE 4.347713 4.278816 5.750000
35 CC1=CC=CC2=NC=C(C)C(=C12)Cl 36 SAFE 4.838436 4.986669 3.705299
36 CCC(CC)([CH](OC(N)=O)C1=CC=CC=C1)C2=CC=CC=C2 37 SAFE 5.392502 4.970603 3.500000
37 ON=C(CC1=CC=CC=C1)[CH](C#N)C2=CC=CC=C2 38 SAFE 4.571970 5.076957 4.839781
38 O[CH](CC1=CC=CC=C1)C2=CC=CC=C2 39 SAFE 4.466226 5.508138 3.966036
39 COC1=CC=C(CC2=CC=C(OC)C=C2)C=C1 40 SAFE 4.738224 5.536254 3.733636
40 CN(C)[CH](C1=CC=CC=C1)C2=C(C)C=CC=C2 41 TOXIC 5.476099 5.190425 3.584815
41 COC1=CC(=C(N)C(=C1)[N+]([O-])=O)[N+]([O-])=O 42 SAFE 4.300471 2.241414 4.416720
42 NN=C(C1=CC=CC=C1)C2=CC=CC=C2 43 SAFE 4.684622 5.386551 4.770296
43 COC1=CC=C(C=C1)C=NO 44 SAFE 3.870875 4.555242 5.750000
44 C1=CC=C(C=C1)C(N=C(C2=CC=CC=C2)C3=CC=CC=C3)C4=... 45 SAFE 5.410187 3.985630 3.000000
45 C1=CC=C(C=C1)N=C(C2=CC=CC=C2)C3=CC=CC=C3 46 SAFE 5.169822 4.727627 3.000000
46 CC1=C(C2=CC=CC=C2)C(=C3C=CC=CC3=N1)O 47 SAFE 4.969107 5.217189 4.289910
47 CCC1=[O+][Cu]2([O+]=C(CC)C1)[O+]=C(CC)CC(=[O+]... 48 SAFE 4.985559 2.823635 5.000000
48 OC(=O)[CH](CC1=CC=CC=C1)C2=CC=CC=C2 49 SAFE 4.308157 5.074406 5.240490
49 CCC1=C(N)C=C(C)N=C1 50 SAFE 4.725621 4.817645 4.612651

Filter out the TOXIC compounds based on their hERG classification.

mols_admet.filter(items=['SMILES', 'NCI_ID', 'HERG classification', 'HERG activity']).query('`HERG classification` == "TOXIC"')
SMILES NCI_ID HERG classification HERG activity
12 NC1=CC=NC2=C1C=CC(=C2)Cl 13 TOXIC 5.067218
13 CCCCCC[CH]1CCCCN1 14 TOXIC 5.536671
40 CN(C)[CH](C1=CC=CC=C1)C2=C(C)C=CC=C2 41 TOXIC 5.476099

Isoelectric point calculation

from chemaxon.calculations import isoelectric_point

glycine_mol = import_mol('C(C(=O)O)N')

res = isoelectric_point(glycine_mol)

print('Isoelectric point of glycine: ', res.isoelectric_point)
print('Charge distributions: \n', res.charge_distributions)
print()
Isoelectric point of glycine:  5.7764474458532655
Charge distributions: 
 [IsoelectricChargeResult(charge=0.9950975988329285, ph=0.0), IsoelectricChargeResult(charge=0.9846598571913905, ph=0.5), IsoelectricChargeResult(charge=0.9530476031965848, ph=1.0), IsoelectricChargeResult(charge=0.8652081089245873, ph=1.5), IsoelectricChargeResult(charge=0.6699470037872222, ph=2.0), IsoelectricChargeResult(charge=0.39094357485128406, ph=2.5), IsoelectricChargeResult(charge=0.16873158347385953, ph=3.0), IsoelectricChargeResult(charge=0.06031498852059858, ph=3.5), IsoelectricChargeResult(charge=0.01988860103811574, ph=4.0), IsoelectricChargeResult(charge=0.00635972354141523, ph=4.5), IsoelectricChargeResult(charge=0.001968209077158334, ph=5.0), IsoelectricChargeResult(charge=0.0004596767143161262, ph=5.5), IsoelectricChargeResult(charge=-0.0003717231072617455, ph=6.0), IsoelectricChargeResult(charge=-0.001750806299997576, ph=6.5), IsoelectricChargeResult(charge=-0.0056967698521620536, ph=7.0), IsoelectricChargeResult(charge=-0.017851775392175973, ph=7.5), IsoelectricChargeResult(charge=-0.0543709597689046, ph=8.0), IsoelectricChargeResult(charge=-0.1538533154447136, ph=8.5), IsoelectricChargeResult(charge=-0.36507675979409415, ph=9.0), IsoelectricChargeResult(charge=-0.645174948815991, ph=9.5), IsoelectricChargeResult(charge=-0.8518505236262278, ph=10.0), IsoelectricChargeResult(charge=-0.9478702541903009, ph=10.5), IsoelectricChargeResult(charge=-0.982905806743576, ph=11.0), IsoelectricChargeResult(charge=-0.9945304099769148, ph=11.5), IsoelectricChargeResult(charge=-0.9982638707242296, ph=12.0), IsoelectricChargeResult(charge=-0.9994503352013361, ph=12.5), IsoelectricChargeResult(charge=-0.9998261153749171, ph=13.0), IsoelectricChargeResult(charge=-0.9999450063148564, ph=13.5), IsoelectricChargeResult(charge=-0.9999826088158388, ph=14.0)]

from chemaxon.calculations import PhRange

res = isoelectric_point(glycine_mol, ph_range=PhRange(0, 14, 0.5))

rows = []
for charge_res in res.charge_distributions:
    rows.append({'pH': charge_res.ph, 'Charge distribution': charge_res.charge})

df_isoelectric = pandas.DataFrame(rows)
df_isoelectric
pH Charge distribution
0 0.0 0.995098
1 0.5 0.984660
2 1.0 0.953048
3 1.5 0.865208
4 2.0 0.669947
5 2.5 0.390944
6 3.0 0.168732
7 3.5 0.060315
8 4.0 0.019889
9 4.5 0.006360
10 5.0 0.001968
11 5.5 0.000460
12 6.0 -0.000372
13 6.5 -0.001751
14 7.0 -0.005697
15 7.5 -0.017852
16 8.0 -0.054371
17 8.5 -0.153853
18 9.0 -0.365077
19 9.5 -0.645175
20 10.0 -0.851851
21 10.5 -0.947870
22 11.0 -0.982906
23 11.5 -0.994530
24 12.0 -0.998264
25 12.5 -0.999450
26 13.0 -0.999826
27 13.5 -0.999945
28 14.0 -0.999983 
#!{sys.executable} -m pip install matplotlib

import matplotlib

df_isoelectric.plot(x='pH', y='Charge distribution', kind='line', marker='o', title='Charge Distribution vs pH for Glycine', grid=True)
<Axes: title={'center': 'Charge Distribution vs pH for Glycine'}, xlabel='pH'>

png

Conformer calculation

#helper function to show the result of the conformer calculation
def display_result(result: list):
    for res in result:
        display(res[0])
        print(res[1])
        
from chemaxon.io import import_mol
from chemaxon.calculations import conformers

mol = import_mol('OC1CC(O)CCC1')
result = conformers(mol)
display_result(result)

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11.50232067802384

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20.29704226268518

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20.33192870563859

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20.34937325920659

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20.36135519975411

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20.445364147288547

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20.450805783762974

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20.635321166833943

from chemaxon.calculations import ConformerOptions, ConformerForceField, EnergyUnit, OptimizationLimit

options = ConformerOptions()
options.max_number_of_conformers = 3
options.energy_unit = EnergyUnit.KJ_PER_MOL
options.force_field = ConformerForceField.MMFF94
options.optimization_limit = OptimizationLimit.VERY_STRICT
result = conformers(mol, options=options)
display_result(result)

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184.20114759569609

Solubility calculation

from chemaxon.io import import_mol
from chemaxon.calculations import solubility

mol_str = 'CC(=O)OC1=CC=CC=C1C(O)=O'
mol = import_mol(mol_str)
solubility(mol)
-1.8489350401969602

from chemaxon.calculations import solubility_ph_range, PhRange, SolubilityUnit

result = solubility_ph_range(mol, PhRange(0.0, 14.0, 1.0), unit = SolubilityUnit.MOL_PER_L)
rows = []
for res in result:
    rows.append({'pH': res.ph, 'Solubility': res.solubility})

df_solubility = pandas.DataFrame(rows)
df_solubility
pH Solubility
0 0.0 0.014166
1 1.0 0.014215
2 2.0 0.014705
3 3.0 0.019608
4 4.0 0.068644
5 5.0 0.558996
6 6.0 1.416006
7 7.0 1.416006
8 8.0 1.416006
9 9.0 1.416006
10 10.0 1.416006
11 11.0 1.416006
12 12.0 1.416006
13 13.0 1.416006
14 14.0 1.416006

Tautomer calculation

from chemaxon.io import import_mol 
from chemaxon.calculations import (all_tautomers, dominant_tautomer_distribution, 
    canonical_tautomer, major_tautomer, TautomerAdvancedOptions)

mol = import_mol('OC1=NC=CC2=CC=NC=C12')
result = all_tautomers(mol)
for res in result:
    display(res)

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#helper function to show the result of tautomer calculation
def display_tautomer_result(result: list):
    for res in result:
        display(res[0])
        print(res[1])

mol = import_mol('CC1=CNCC(O)=C1')
result = dominant_tautomer_distribution(mol)
display_tautomer_result(result)

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98.77065813615263

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1.2293414950976624

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3.687497028338006e-07

mol = import_mol('OC1N(S)CC=CC1=O')
canonical_tautomer(mol, normal=True)

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mol = import_mol('OC1=NC=CC2=CC=NC=C12')
major_tautomer(mol, ph=14.0)

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mol = import_mol('CC(=O)\\C=C(/O)CC1=CC=CC=C1')
advanced_options = TautomerAdvancedOptions()
advanced_options.protect_double_bond_stereo = True
major_tautomer(mol, options=advanced_options)

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Resonance calculation

The resonance plugin enumerates the resonant (mesomeric) structures of a molecule — the set of Lewis structures that differ only in the distribution of the electrons, not in the position of the atoms. By default resonance_structures returns only the major contributors, filters out symmetrical duplicates and generates at most 1000 structures.

from chemaxon.io import import_mol
from chemaxon.calculations import resonant_structures, canonical_resonant_structure

mol = import_mol('[O-]C(=O)C1=CC=CC=C1')
result = resonant_structures(mol)
for res in result:
    display(res)

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Pass major_contributors_only=False to obtain every contributor, and symmetry_filtering=False to keep symmetrical duplicates. max_structures caps the number of generated structures.

mol = import_mol('[O-]C(=O)C([O-])=O')
all_contributors = resonant_structures(mol, major_contributors_only=False, symmetry_filtering=False)
major_contributors = resonant_structures(mol)
print(f'{len(all_contributors)} contributors in total, {len(major_contributors)} of them major')
97 contributors in total, 3 of them major

canonical_resonant_structure returns a single, canonical representative form, which is useful for example for structure indexing and searching. Set clean_structure=True to lay the result out in 2D.

mol = import_mol('[O-]C(=O)C1=CC=CC=C1')
canonical_resonant_structure(mol, clean_structure=True)

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Geometrical calculations

Polar Surface Area (2D)

For more information about this plugin, check the public documentation.

from chemaxon.calculations import polar_surface_area

mol_psa = import_mol('CC(=O)OC1=CC=CC=C1C(O)=O')
psa_value = polar_surface_area(mol_psa)

print('The molecule: (aspirin)')
display(mol_psa)
print('Polar Surface Area value: ' + str(psa_value))
The molecule: (aspirin)

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Polar Surface Area value: 63.6

Hückel Analysis

Several methods are available that are based on the Hückel Molecular Orbital (HMO) theorem/method. An example for each on a chemaxon.Molecule (these can also be calculated on specific pH values):

from IPython.display import SVG
from chemaxon.calculations import (hmo_electrophilic_localization_energy,
                                    hmo_nucleophilic_localization_energy, hmo_electron_density,
                                    hmo_charge_density, hmo_electrophilic_order,
                                    hmo_nucleophilic_order, hmo_pi_energy)
from chemaxon.io import import_mol, visualize_atom_values

mol_hmo = import_mol('CN1C=NC2=C1C(=O)NC(=O)N2C')

electr_localization_energy = hmo_electrophilic_localization_energy(mol_hmo)
nucleo_localization_energy = hmo_nucleophilic_localization_energy(mol_hmo)
electr_dens = hmo_electron_density(mol_hmo)
ch_dens = hmo_charge_density(mol_hmo)
electr_ord = hmo_electrophilic_order(mol_hmo)
nucl_ord = hmo_nucleophilic_order(mol_hmo)
pi_energy = hmo_pi_energy(mol_hmo)

print('Pi energy: '+ str(pi_energy))
Pi energy: 23.18

print('L(+) localization energy by atom indices:')
SVG(visualize_atom_values(mol_hmo, electr_localization_energy))
L(+) localization energy by atom indices:

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print('L(-) localization energy by atom indices:')
SVG(visualize_atom_values(mol_hmo, nucleo_localization_energy))
L(-) localization energy by atom indices:

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print('Electrophilic densities by atom indices:')
SVG(visualize_atom_values(mol_hmo, electr_dens))
Electrophilic densities by atom indices:

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print('Charge densities by atom indices:')
SVG(visualize_atom_values(mol_hmo, ch_dens))
Charge densities by atom indices:

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print('E(+) orders by atom indices:')
SVG(visualize_atom_values(mol_hmo, electr_ord))
E(+) orders by atom indices:

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print('Nu(-) orders by atom indices:')
SVG(visualize_atom_values(mol_hmo, nucl_ord))
Nu(-) orders by atom indices:

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Molecular Surface Area 3D

There are two versions available for 3D molecular surface area calculation:

from chemaxon.io import import_mol, export_mol
from chemaxon.calculations import van_der_waals_surface_area, solvent_accessible_surface_area

aspirin = import_mol('CC(=O)OC1=CC=CC=C1C(O)=O')
vdw_result = van_der_waals_surface_area(aspirin)
asa_result = solvent_accessible_surface_area(aspirin)

print('van der Waals surface area of aspirin: ' + str(vdw_result.surface_area))
print('ASA surface area of aspirin: ' + str(asa_result.surface_area))
print('ASA of atoms with positive partial charge: ' + str(asa_result.asa_plus))
print('ASA of atoms with negative partial charge: ' + str(asa_result.asa_negative))
print('ASA of hydrophobic atoms (|partial charge| < 0.125): ' + str(asa_result.asa_hydrophobic))
print('ASA of polar atoms (|partial charge| >= 0.125): ' + str(asa_result.asa_polar))
van der Waals surface area of aspirin: 255.2
ASA surface area of aspirin: 387.94
ASA of atoms with positive partial charge: 249.15
ASA of atoms with negative partial charge: 138.79
ASA of hydrophobic atoms (|partial charge| < 0.125): 291.29
ASA of polar atoms (|partial charge| >= 0.125): 96.66

from IPython.display import SVG
from chemaxon.io import visualize_atom_values

print('van der Waals increments of aspirin:')
SVG(visualize_atom_values(aspirin, vdw_result.increments))
van der Waals increments of aspirin:

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Refractivity calculation

Molar refractivity is a descriptor of the molecular volume and the London dispersive forces playing a role in drug-receptor interactions. For more information, check the public documentation.

from chemaxon.io import import_mol
from chemaxon.calculations import refractivity

mol_refr = import_mol('CC(=O)OC1=CC=CC=C1C(O)=O')  # aspirin

result = refractivity(mol_refr)

print('The molecule: (aspirin)')
display(mol_refr)
print('Molar refractivity:', result.molar_refractivity)
The molecule: (aspirin)

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Molar refractivity: 44.45

from IPython.display import SVG
from chemaxon.io import visualize_atom_values

print('Atomic refractivity increments:')
SVG(visualize_atom_values(mol_refr, result.refractivity_values, value_attribute='refractivity'))
Atomic refractivity increments:

svg

from IPython.display import SVG
from chemaxon.io import visualize_atom_values

print('Atomic hydrogen refractivity increments:')
SVG(visualize_atom_values(mol_refr, result.refractivity_values, value_attribute='hydrogen_refractivity'))
Atomic hydrogen refractivity increments:

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Geometrical Descriptors calculation

Molecule-scope 3D descriptors of a conformation (Dreiding and MMFF94 strain energy, minimal/maximal projection area and radius, minZ/maxZ, van der Waals volume), plus atom-level distance/angle/dihedral measurements and per-atom steric hindrance. A 3D conformer is generated automatically for input without 3D coordinates. For more information, check the public documentation.

from chemaxon.io import import_mol
from chemaxon.calculations import geometrical_descriptors

mol_geom = import_mol('CC(=O)OC1=CC=CC=C1C(O)=O')  # aspirin

result = geometrical_descriptors(mol_geom)

print('The molecule: (aspirin)')
display(mol_geom)
print('Dreiding energy:', result.dreiding_energy)
print('MMFF94 energy:', result.mmff94_energy)
print('Minimal projection area:', result.minimal_projection_area)
print('Maximal projection area:', result.maximal_projection_area)
print('Volume:', result.volume)
The molecule: (aspirin)

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Dreiding energy: 29.78
MMFF94 energy: 42.36
Minimal projection area: 33.28
Maximal projection area: 56.0
Volume: 154.85

The plugin also exposes standalone atom-level measurements (distance, angle, dihedral) and steric hindrance:

from chemaxon.calculations import distance, angle, dihedral, steric_hindrance

print('Distance between atom 0 and atom 1:', distance(mol_geom, 0, 1))
print('Angle at atom 1 (atoms 0-1-2):', angle(mol_geom, 0, 1, 2))
print('Dihedral of atoms 0-1-2-3:', dihedral(mol_geom, 0, 1, 2, 3))
Distance between atom 0 and atom 1: 6.11
Angle at atom 1 (atoms 0-1-2): 118.56
Dihedral of atoms 0-1-2-3: -178.6

from IPython.display import SVG
from chemaxon.io import visualize_atom_values

print('Steric hindrance per atom:')
SVG(visualize_atom_values(mol_geom, steric_hindrance(mol_geom)))
Steric hindrance per atom:

svg

Structural Frameworks calculation

Reduces a molecule to a structural framework (scaffold), such as the Bemis-Murcko scaffold or a ring system, by stripping side chains, generalizing atoms/bonds or selecting ring systems. For more information, check the public documentation.

from chemaxon.io import import_mol
from chemaxon.calculations import structural_framework, FrameworkType

mol_scaffold = import_mol('CC(=O)OC1=CC=CC=C1C(O)=O')  # aspirin

print('The molecule: (aspirin)')
display(mol_scaffold)

bemis_murcko = structural_framework(mol_scaffold, framework_type=FrameworkType.BEMIS_MURCKO)
print('Bemis-Murcko framework:')
display(bemis_murcko)

ring_systems = structural_framework(mol_scaffold, framework_type=FrameworkType.ALL_RING_SYSTEMS)
print('All ring systems:')
display(ring_systems)
The molecule: (aspirin)

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Bemis-Murcko framework:

svg

All ring systems:

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Hydrogen Bond Donor/Acceptor (HBDA) calculation

For more information, check the public documentation.

from chemaxon.io import import_mol
from chemaxon.calculations import hbda

mol_hbda = import_mol('CC(=O)NC1=CC=C(O)C=C1')  # paracetamol

result = hbda(mol_hbda)

print('The molecule: (paracetamol)')
display(mol_hbda)
print('Donor atom count:', result.donor_atom_count)
print('Acceptor atom count:', result.acceptor_atom_count)
print('Donor site count:', result.donor_site_count)
print('Acceptor site count:', result.acceptor_site_count)
The molecule: (paracetamol)

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Donor atom count: 2
Acceptor atom count: 2
Donor site count: 2
Acceptor site count: 4

from IPython.display import SVG
from chemaxon.io import visualize_atom_values

print('Per-atom donor counts:')
SVG(visualize_atom_values(mol_hbda, result.atom_values, value_attribute='donor_count'))
Per-atom donor counts:

svg

from IPython.display import SVG
from chemaxon.io import visualize_atom_values

print('Per-atom acceptor counts:')
SVG(visualize_atom_values(mol_hbda, result.atom_values, value_attribute='acceptor_count'))
Per-atom acceptor counts:

svg

The donor/acceptor counts can also be calculated for the major microspecies over a pH range:

from chemaxon.calculations import hbda_ph_range, PhRange
import matplotlib.pyplot as pyplt

ph_results = hbda_ph_range(mol_hbda, PhRange(0, 14, 0.5))
pyplt.plot([r.ph for r in ph_results], [r.donor_count for r in ph_results], label='Donor count')
pyplt.plot([r.ph for r in ph_results], [r.acceptor_count for r in ph_results], label='Acceptor count')
pyplt.title('HBDA counts by pH (paracetamol)')
pyplt.xlabel('pH')
pyplt.ylabel('Count')
pyplt.legend()
<matplotlib.legend.Legend at 0x713e18dc5010>

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