def logKOH_T(self):
from .matrix.koh import kohX
# KOH 温度依附性模型, 有温度参数
#CAS-Number X% EHOMO Mor29u NdsCH GATS1e X3A 1/T SdsCH nR=Cp F02[F-Br] RDF015m BIC1 SpMin8_Bh(p) NssssC
abstract_value = self.dragon_model.extractparameter([
"X%", "EHOMO", "Mor29u", "NdsCH", "GATS1e", "X3A", "SdsCH",
"nR=Cp", "F02[F-Br]", "RDF015m", "BIC1", "SpMin8_Bh(p)", "NssssC"
])
for smilenum in abstract_value:
if smilenum not in self.predict_result:
self.predict_result[smilenum] = defaultdict(dict)
abstract_value[smilenum]['EHOMO'] = fetch_ehomo(
smilenum, 'logKOH_T')
value = -8.613 - \
0.02100 * abstract_value[smilenum]['X%'] + \
14.38 * abstract_value[smilenum]['EHOMO'] - \
0.6430 * abstract_value[smilenum]['Mor29u'] + \
0.5870 * abstract_value[smilenum]['NdsCH'] + \
0.5870 * abstract_value[smilenum]['GATS1e'] + \
0.5770 * abstract_value[smilenum]['X3A'] - \
0.2450 * abstract_value[smilenum]['SdsCH'] - \
167.0 * (1 / self.T) + \
1.103 * abstract_value[smilenum]['BIC1'] + \
0.1170 * abstract_value[smilenum]['RDF015m'] - \
1.044 * abstract_value[smilenum]['SpMin8_Bh(p)'] + \
0.2390 * abstract_value[smilenum]['nR=Cp'] - \
0.1980 * abstract_value[smilenum]['NssssC'] - \
0.5080 * abstract_value[smilenum]['F02[F-Br]']
self.predict_result[smilenum]['logKOH_T']['value'] = self.round(
value)
chemistry_logger.info('koh_t(%s) dragon: %s' %
(smilenum, abstract_value[smilenum]))
x = matrix([[
abstract_value[smilenum]['X%'],
abstract_value[smilenum]['EHOMO'],
abstract_value[smilenum]['Mor29u'],
abstract_value[smilenum]['NdsCH'],
abstract_value[smilenum]['GATS1e'],
abstract_value[smilenum]['X3A'], 1.0 / self.T,
abstract_value[smilenum]['SdsCH'],
abstract_value[smilenum]['nR=Cp'],
abstract_value[smilenum]['F02[F-Br]'],
abstract_value[smilenum]['RDF015m'],
abstract_value[smilenum]['BIC1'],
abstract_value[smilenum]['SpMin8_Bh(p)'],
abstract_value[smilenum]['NssssC']
]])
williams = self.get_williams(kohX, x)
self.predict_result[smilenum]['logKOH_T'].update(williams)
def logKOH_T(self):
from .matrix.koh import kohX
# KOH 温度依附性模型, 有温度参数
#CAS-Number X% EHOMO Mor29u NdsCH GATS1e X3A 1/T SdsCH nR=Cp F02[F-Br] RDF015m BIC1 SpMin8_Bh(p) NssssC
abstract_value = self.dragon_model.extractparameter([
"X%", "EHOMO", "Mor29u", "NdsCH", "GATS1e", "X3A", "SdsCH",
"nR=Cp", "F02[F-Br]", "RDF015m", "BIC1", "SpMin8_Bh(p)", "NssssC"])
for smilenum in abstract_value:
if smilenum not in self.predict_result:
self.predict_result[smilenum] = defaultdict(dict)
abstract_value[smilenum]['EHOMO'] = fetch_ehomo(smilenum, 'logKOH_T')
value = -8.613 - \
0.02100 * abstract_value[smilenum]['X%'] + \
14.38 * abstract_value[smilenum]['EHOMO'] - \
0.6430 * abstract_value[smilenum]['Mor29u'] + \
0.5870 * abstract_value[smilenum]['NdsCH'] + \
0.5870 * abstract_value[smilenum]['GATS1e'] + \
0.5770 * abstract_value[smilenum]['X3A'] - \
0.2450 * abstract_value[smilenum]['SdsCH'] - \
167.0 * (1 / self.T) + \
1.103 * abstract_value[smilenum]['BIC1'] + \
0.1170 * abstract_value[smilenum]['RDF015m'] - \
1.044 * abstract_value[smilenum]['SpMin8_Bh(p)'] + \
0.2390 * abstract_value[smilenum]['nR=Cp'] - \
0.1980 * abstract_value[smilenum]['NssssC'] - \
0.5080 * abstract_value[smilenum]['F02[F-Br]']
self.predict_result[smilenum]['logKOH_T']['value'] = self.round(value)
chemistry_logger.info('koh_t(%s) dragon: %s' % (smilenum, abstract_value[smilenum]))
x = matrix([[abstract_value[smilenum]['X%'],
abstract_value[smilenum]['EHOMO'],
abstract_value[smilenum]['Mor29u'],
abstract_value[smilenum]['NdsCH'],
abstract_value[smilenum]['GATS1e'],
abstract_value[smilenum]['X3A'],
1.0 / self.T,
abstract_value[smilenum]['SdsCH'],
abstract_value[smilenum]['nR=Cp'],
abstract_value[smilenum]['F02[F-Br]'],
abstract_value[smilenum]['RDF015m'],
abstract_value[smilenum]['BIC1'],
abstract_value[smilenum]['SpMin8_Bh(p)'],
abstract_value[smilenum]['NssssC']
]])
williams = self.get_williams(kohX, x)
self.predict_result[smilenum]['logKOH_T'].update(williams)
def logKOH(self):
from .matrix.koh_298k import koh_298kX
# KOH 298K预测模型, 无温度参数
#CAS-Number EHOMO AMW NdsCH Mor14i nP nR=Cp X% nRCHO C-020 SpMaxA_AEA(dm) nCbH CATS2D_03_DL
abstract_value = self.dragon_model.extractparameter([
"EHOMO", "AMW", "NdsCH", "Mor14i", "nP", "nR=Cp", "X%", "nRCHO",
"C-020", "SpMaxA_AEA(dm)", "nCbH", "CATS2D_03_DL"
])
for smilenum in abstract_value:
if smilenum not in self.predict_result:
self.predict_result[smilenum] = defaultdict(dict)
abstract_value[smilenum]['EHOMO'] = fetch_ehomo(smilenum, 'logKOH')
value = -6.511 + \
15.85 * abstract_value[smilenum]['EHOMO'] - \
0.03800 * abstract_value[smilenum]['AMW'] + \
0.1300 * abstract_value[smilenum]['NdsCH'] + \
0.1630 * abstract_value[smilenum]['Mor14i'] + \
0.7790 * abstract_value[smilenum]['nP'] + \
0.3170 * abstract_value[smilenum]['nR=Cp'] - \
0.01900 * abstract_value[smilenum]['X%'] + \
0.3930 * abstract_value[smilenum]['nRCHO'] + \
0.5890 * abstract_value[smilenum]['C-020'] - \
0.4550 * abstract_value[smilenum]['SpMaxA_AEA(dm)'] - \
0.05600 * abstract_value[smilenum]['nCbH'] + \
0.1410 * abstract_value[smilenum]['CATS2D_03_DL']
self.predict_result[smilenum]['logKOH']['value'] = self.round(
value)
chemistry_logger.info('koh(%s) dragon: %s' %
(smilenum, abstract_value[smilenum]))
x = matrix([[
abstract_value[smilenum]['EHOMO'],
abstract_value[smilenum]['AMW'],
abstract_value[smilenum]['NdsCH'],
abstract_value[smilenum]['Mor14i'],
abstract_value[smilenum]['nP'],
abstract_value[smilenum]['nR=Cp'],
abstract_value[smilenum]['X%'],
abstract_value[smilenum]['nRCHO'],
abstract_value[smilenum]['C-020'],
abstract_value[smilenum]['SpMaxA_AEA(dm)'],
abstract_value[smilenum]['nCbH'],
abstract_value[smilenum]['CATS2D_03_DL']
]])
williams = self.get_williams(koh_298kX, x)
self.predict_result[smilenum]['logKOH'].update(williams)
def logKOH(self):
from .matrix.koh_298k import koh_298kX
# KOH 298K预测模型, 无温度参数
#CAS-Number EHOMO AMW NdsCH Mor14i nP nR=Cp X% nRCHO C-020 SpMaxA_AEA(dm) nCbH CATS2D_03_DL
abstract_value = self.dragon_model.extractparameter([
"EHOMO", "AMW", "NdsCH", "Mor14i", "nP", "nR=Cp",
"X%", "nRCHO", "C-020", "SpMaxA_AEA(dm)",
"nCbH", "CATS2D_03_DL"])
for smilenum in abstract_value:
if smilenum not in self.predict_result:
self.predict_result[smilenum] = defaultdict(dict)
abstract_value[smilenum]['EHOMO'] = fetch_ehomo(smilenum, 'logKOH')
value = -6.511 + \
15.85 * abstract_value[smilenum]['EHOMO'] - \
0.03800 * abstract_value[smilenum]['AMW'] + \
0.1300 * abstract_value[smilenum]['NdsCH'] + \
0.1630 * abstract_value[smilenum]['Mor14i'] + \
0.7790 * abstract_value[smilenum]['nP'] + \
0.3170 * abstract_value[smilenum]['nR=Cp'] - \
0.01900 * abstract_value[smilenum]['X%'] + \
0.3930 * abstract_value[smilenum]['nRCHO'] + \
0.5890 * abstract_value[smilenum]['C-020'] - \
0.4550 * abstract_value[smilenum]['SpMaxA_AEA(dm)'] - \
0.05600 * abstract_value[smilenum]['nCbH'] + \
0.1410 * abstract_value[smilenum]['CATS2D_03_DL']
self.predict_result[smilenum]['logKOH']['value'] = self.round(value)
chemistry_logger.info('koh(%s) dragon: %s' % (smilenum, abstract_value[smilenum]))
x = matrix([[abstract_value[smilenum]['EHOMO'],
abstract_value[smilenum]['AMW'],
abstract_value[smilenum]['NdsCH'],
abstract_value[smilenum]['Mor14i'],
abstract_value[smilenum]['nP'],
abstract_value[smilenum]['nR=Cp'],
abstract_value[smilenum]['X%'],
abstract_value[smilenum]['nRCHO'],
abstract_value[smilenum]['C-020'],
abstract_value[smilenum]['SpMaxA_AEA(dm)'],
abstract_value[smilenum]['nCbH'],
abstract_value[smilenum]['CATS2D_03_DL']]])
williams = self.get_williams(koh_298kX, x)
self.predict_result[smilenum]['logKOH'].update(williams)
