InChI=1/C19H20N2O3/c22-18-10-21-12-5-11(18)6-13(21)8-14(7-12)24-19(23)16-9-20-17-4-2-1-3-15(16)17/h1-4,9,11-14,20H,5-8,10H2

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Search term: UKTAZPQNNNJVKR-UHFFFAOYAK
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Inherent Properties, Identifiers and References

ChemSpider ID:	3036
Empirical Formula:	C₁₉H₂₀N₂O₃
Molecular Weight:	324.3737
Nominal Mass:	324 Da
Average Mass:	324.3737 Da
Monoisotopic Mass:	324.147393 Da

Systematic Name:

SMILES:

O=C5C1CC4N(C(C1)CC(OC(=O)c3c2ccccc2nc3)C4)C5

InChI:

InChI=1/C19H20N2O3/c22-18-10-21-12-5-11(18)6-13(21)8-14(7-12)24-19(23)16-9-20-17-4-2-1-3-15(16)17/h1-4,9,11-14,20H,5-8,10H2

InChIKey:

UKTAZPQNNNJVKR-UHFFFAOYAK

Std. InChI:

InChI=1S/C19H20N2O3/c22-18-10-21-12-5-11(18)6-13(21)8-14(7-12)24-19(23)16-9-20-17-4-2-1-3-15(16)17/h1-4,9,11-14,20H,5-8,10H2

Std. InChIKey:

UKTAZPQNNNJVKR-UHFFFAOYSA-N

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Supplemental Information

Links & References

William Bains, Antranig Basman and Cat White. hERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study, Progress in Biophysics and Molecular Biology 2004, 86 (2), 205-233

[DOI: 10.1016/j.pbiomolbio.2003.09.001]

The authors describe the Tapplication of genetic programming, an evolutionary computing method, to predicting whether small molecules will block the HERG cardiac potassium channel. Models based on a molecular fragment-based descriptor set achieve an accuracy of 85–90% in predicting whether the IC50 of a ‘blind’ set of compounds is <1 μM. The datasets are available from QSAR World at http://www.qsarworld.com/qsar-datasets-bains.php

Hongmao Sun. An Accurate and Interpretable Bayesian Classification Model for Prediction of hERG Liability, ChemMedChem 2006, 1(3), 315-322.

[DOI: 10.1002/cmdc.200500047]

Presented herein is a naive Bayes classifier to categorize hERG blockers into active and inactive classes, by using a universal, generic molecular descriptor system. The naive Bayes classifier was built from a training set containing 1979 corporate compounds, and exhibited an ROC accuracy of 0.87. The model was validated on an external test set of 66 drugs, of which 58 were correctly classified. The dataset is available from QSAR World: http://www.qsarworld.com/qsar-datasets-sun.php

György M. Keserü. Prediction of hERG potassium channel affinity by traditional and hologram QSAR methods, Bioorg. Med. Chem. Lett. 2003, 13(16), 2773-2775.

68 Compounds and their hERG pIC50 values have been provided in the paper."A-56268" was retrieved as "Clarithromycin" and "Hismanal" as "Astemizole" from ChemIDPlus. "RP-58866" was not found at ChemIDPlus. Thus, 67 compounds and their pIC50 values have been provided here. The compounds used as a 'test set' have been labelled so in the AMP file. The dataset is available from the QSAR World website: http://www.qsarworld.com/qsar-datasets-keseru.php

Andrea Cavalli, Elisabetta Poluzzi, Fabrizio De Ponti, and Maurizio Recanatini. Toward a Pharmacophore for Drugs Inducing the Long QT Syndrome: Insights from a CoMFA Study of hERG K+ Channel Blockers, J. Med. Chem. 2002, 45(18), 3844-3853.

[DOI: 10.1021/jm0208875]

The blockade of HERG K+ channels is one of the most important molecular mechanisms through which QT-prolonging drugs increase cardiac action potential duration. Since QT prolongation is one of the most undesirable side effects of drugs, the authors first tried to identify the minimum set of molecular features responsible for this action and then attempted to develop a quantitative model correlating the 3D stereoelectronic characteristics of the molecules with their HERG blocking potency. The datasets can be downloaded from QSAR World here: http://www.qsarworld.com/qsar-datasets-cavalli.php

Sean Ekins, Konstantin V. Balakin, Nikolay Savchuk, and Yan Ivanenkov. Insights for Human Ether-a-Go-Go-Related Gene Potassium Channel Inhibition Using Recursive Partitioning and Kohonen and Sammon Mapping Techniques, J. Med. Chem. 2006, 49(17), 5059-5071.

[DOI: 10.1021/jm060076r]

We have collated literature data for 99 diverse hERG inhibitors to generate Kohonen maps, Sammon maps, and recursive partitioning models. Our aim was to investigate whether these computational models could be used either individually or together in a consensus approach to predict the binding of a prospectively selected test set of 35 diverse molecules and at the same time to offer further insights into hERG inhibition. The recursive partitioning model provided a quantitative prediction, which was markedly improved when Tanimoto similarity was included as a filter to remove molecules from the test set that were too dissimilar to the training set (r2 = 0.83, Spearman rho = 0.75, p = 0.0003 for the 18 remaining molecules, >0.77 similarity).The datasets can be downloaded from the WSAR world pages: http://www.qsarworld.com/qsar-datasets-ekins.php

Daniel F. Veber, Stephen R. Johnson, Hung-Yuan Cheng, Brian R. Smith, Keith W. Ward, and Kenneth D. Kopple. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates, J. Med. Chem. 2002, 45(12), 2615-2623

[DOI: 10.1021/jm020017n]

275 drugs and their human oral bioavalability values have been given in the supplementary material of the paper. These values have in turn been taken from Goodman & Gilman's The Pharmacological Basis of Therapeutics, VIII and X Editions. The datasets are available from the QSAR World pages: http://www.qsarworld.com/qsar-datasets-veber.php

Katsumi Yoshida and Tomoko Niwa. Quantitative Structure-Activity Relationship Studies on Inhibition of HERG Potassium Channels, J. Chem. Inf. Model. 2006, 46(3, 1371-1378.

[DOI: 10.1021/ci050450g]

Using descriptors which have clear physicochemical meanings and are familiar to medicinal chemists, the authors carried out 2D-quantitative structure−activity relationship (2D-QSAR) studies on 104 HERG channel blockers with diverse structures collected from the literature, and formulated interpretable models to guide chemical-modification studies and virtual screening. The links to the dataset are on QSAR world at this URL: http://www.qsarworld.com/qsar-datasets-niwa.php

Minghu Song and Matthew Clark. Development and Evaluation of an in Silico Model for hERG Binding, J. Chem. Inf. Model. 2006, 46(1), 392-400.

[DOI: 10.1021/ci050308f]

A diverse set of 90 compounds with hERG IC50 inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r2 of 0.912 and 0.848 for the training and testing data sets, respectively. The following URL can access the datasets at the QSAR world pages: http://www.qsarworld.com/qsar-datasets-song.php

Names and Synonyms

Validated by Experts, Validated by Users, Non-Validated, Removed by Users, Redirected by Users, Redirect Approved by Experts

1H-indole-3-carboxylic acid, octahydro-3-oxo-2,6-methano-2H-quinolizin-8-yl ester

3-oxooctahydro-2H-2,6-methanoquinolizin-8-yl 1H-indole-3-carboxylate

115956-12-2 [RN]

1H-Indole-3-carboxylic acid 10-oxo-8-aza-tricyclo[5.3.1.0*3,8*]undec-5-yl ester

dolasetron [Wiki]

Database ID(s)

Validated by Experts, Validated by Users, Non-Validated, Removed by Users, Redirected by Users, Redirect Approved by Experts

ACD/Labs Predicted Properties

ACD/LogP:	2.82	# of Rule of 5 Violations:	0
ACD/LogD (pH 5.5):	2	ACD/LogD (pH 7.4):	2.79
ACD/BCF (pH 5.5):	12.42	ACD/BCF (pH 7.4):	76.85
ACD/KOC (pH 5.5):	123.36	ACD/KOC (pH 7.4):	763.63
#H bond acceptors:	5	#H bond donors:	1
#Freely Rotating Bonds:	3	Polar Surface Area:	51.54 Å²
Index of Refraction:	1.675	Molar Refractivity:	88.81 cm³
Molar Volume:	236.1 cm³	Polarizability:	35.2 10^-24cm³
Surface Tension:	65.2 dyne/cm	Density:	1.37 g/cm³
Flash Point:	277.4 °C	Enthalpy of Vaporization:	81.15 kJ/mol
Boiling Point:	535.1 °C at 760 mmHg	Vapour Pressure:	1.58E-11 mmHg at 25°C

EPI Summary

            
 Log Octanol-Water Partition Coef (SRC):
    Log Kow (KOWWIN v1.67 estimate) =  2.63

 Boiling Pt, Melting Pt, Vapor Pressure Estimations (MPBPWIN v1.42):
    Boiling Pt (deg C):  474.08  (Adapted Stein & Brown method)
    Melting Pt (deg C):  200.49  (Mean or Weighted MP)
    VP(mm Hg,25 deg C):  1.73E-009  (Modified Grain method)
    Subcooled liquid VP: 1.23E-007 mm Hg (25 deg C, Mod-Grain method)

 Water Solubility Estimate from Log Kow (WSKOW v1.41):
    Water Solubility at 25 deg C (mg/L):  505.3
       log Kow used: 2.63 (estimated)
       no-melting pt equation used

 Water Sol Estimate from Fragments:
    Wat Sol (v1.01 est) =  872.77 mg/L

 ECOSAR Class Program (ECOSAR v0.99h):
    Class(es) found:
       Aliphatic Amines
       Esters

 Henrys Law Constant (25 deg C) [HENRYWIN v3.10]:
   Bond Method :   1.34E-014  atm-m3/mole
   Group Method:   Incomplete
 Henrys LC [VP/WSol estimate using EPI values]:  1.461E-012 atm-m3/mole

 Log Octanol-Air Partition Coefficient (25 deg C) [KOAWIN v1.10]:
  Log Kow used:  2.63  (KowWin est)
  Log Kaw used:  -12.261  (HenryWin est)
      Log Koa (KOAWIN v1.10 estimate):  14.891
      Log Koa (experimental database):  None

 Probability of Rapid Biodegradation (BIOWIN v4.10):
   Biowin1 (Linear Model)         :   0.5689
   Biowin2 (Non-Linear Model)     :   0.4517
 Expert Survey Biodegradation Results:
   Biowin3 (Ultimate Survey Model):   2.3453  (weeks-months)
   Biowin4 (Primary Survey Model) :   3.2985  (days-weeks  )
 MITI Biodegradation Probability:
   Biowin5 (MITI Linear Model)    :   0.3132
   Biowin6 (MITI Non-Linear Model):   0.0418
 Anaerobic Biodegradation Probability:
   Biowin7 (Anaerobic Linear Model): -1.3782
 Ready Biodegradability Prediction:   NO

Hydrocarbon Biodegradation (BioHCwin v1.01):
    Structure incompatible with current estimation method!

 Sorption to aerosols (25 Dec C)[AEROWIN v1.00]:
  Vapor pressure (liquid/subcooled):  1.64E-005 Pa (1.23E-007 mm Hg)
  Log Koa (Koawin est  ): 14.891
   Kp (particle/gas partition coef. (m3/ug)):
       Mackay model           :  0.183 
       Octanol/air (Koa) model:  191 
   Fraction sorbed to airborne particulates (phi):
       Junge-Pankow model     :  0.869 
       Mackay model           :  0.936 
       Octanol/air (Koa) model:  1 

 Atmospheric Oxidation (25 deg C) [AopWin v1.92]:
   Hydroxyl Radicals Reaction:
      OVERALL OH Rate Constant = 226.1754 E-12 cm3/molecule-sec
      Half-Life =     0.047 Days (12-hr day; 1.5E6 OH/cm3)
      Half-Life =     0.567 Hrs
   Ozone Reaction:
      No Ozone Reaction Estimation
   Fraction sorbed to airborne particulates (phi): 0.902 (Junge,Mackay)
    Note: the sorbed fraction may be resistant to atmospheric oxidation

 Soil Adsorption Coefficient (PCKOCWIN v1.66):
      Koc    :  8095
      Log Koc:  3.908 

 Aqueous Base/Acid-Catalyzed Hydrolysis (25 deg C) [HYDROWIN v1.67]:
  Total Kb for pH > 8 at 25 deg C :  9.590E-003  L/mol-sec
  Kb Half-Life at pH 8:       2.290  years  
  Kb Half-Life at pH 7:      22.903  years  

 Bioaccumulation Estimates from Log Kow (BCFWIN v2.17):
   Log BCF from regression-based method = 1.329 (BCF = 21.32)
       log Kow used: 2.63 (estimated)

 Volatilization from Water:
    Henry LC:  1.34E-014 atm-m3/mole  (estimated by Bond SAR Method)
    Half-Life from Model River: 7.869E+010  hours   (3.279E+009 days)
    Half-Life from Model Lake : 8.585E+011  hours   (3.577E+010 days)

 Removal In Wastewater Treatment:
    Total removal:               3.52  percent
    Total biodegradation:        0.11  percent
    Total sludge adsorption:     3.42  percent
    Total to Air:                0.00  percent
      (using 10000 hr Bio P,A,S)

 Level III Fugacity Model:
           Mass Amount    Half-Life    Emissions
            (percent)        (hr)       (kg/hr)
   Air       1.14e-006       1.13         1000       
   Water     14.9            900          1000       
   Soil      84.9            1.8e+003     1000       
   Sediment  0.156           8.1e+003     0          
     Persistence Time: 1.69e+003 hr

SimBioSys LASSO

Descriptors: 0, 0, 0, 1, 1, 0, 0, 5, 1, 0, 0, 0, 9, 4, 5, 0, 9, 2, 2, 1, 0, 0, 0, 0

Category	Target	PDB Code	LASSO Score
Nuclear Hormone Receptors	PPARg, peroxisome proliferator activated receptor	1fm9	0.03
Kinases	SRC, tyrosine kinase SRC	2src	0.01
Other Enzymes	HMGR, hydroxymethylglutaryl-CoA reductase	1hw8	0.01
Other Enzymes	HIVPR, HIV protease	1hpx	0.01
Metalloenzymes	PDE5, phosphodiesterase 5	1xp0	0.01
Nuclear Hormone Receptors	MR, mineralocorticoid receptor	2aa2	0.01
Serine Proteases	Thrombin	1ba8	0.01
Other Enzymes	PARP, poly(ADP-ribose) polymerase	1efy	0.01
Other Enzymes	PNP, purine nucleoside phosphorylase	1b8o	0.00
Other Enzymes	AmpC, AmpC beta-lactamase	1xgj	0.00
Nuclear Hormone Receptors	ER, estrogen receptor; agonist	1l2i	0.00
Metalloenzymes	ACE, angiotensin-converting enzyme	1o86	0.00
Nuclear Hormone Receptors	PR, progesterone receptor	1sr7	0.00
Folate Enzymes	DHFR, dihydrofolate reductase	3dfr	0.00
Kinases	VEGFr2, vascular endothelial growth factor receptor	1vr2	0.00
Other Enzymes	InhA, enoyl ACP reductase	1p44	0.00
Other Enzymes	GPB, glycogen phosphorylase	1a8i	0.00
Other Enzymes	SAHH, S-adenosyl-homocysteine hydrolase	1a7a	0.00
Kinases	FGFr1, fibroblast growth factor receptor kinase	1agw	0.00
Other Enzymes	COX-2, cyclooxygenase-2	1cx2	0.00
Other Enzymes	NA, neuraminidase	1a4g	0.00
Metalloenzymes	ADA, adenosine deaminase	1stw	0.00
Folate Enzymes	GART, glycinamide ribonucleotide transformylase	1c2t	0.00
Kinases	P38 MAP, P38 mitogen activated protein	1kv2	0.00
Nuclear Hormone Receptors	RXRa, retinoic X receptor R	1mvc	0.00
Kinases	TK, thymidine kinase	1kim	0.00
Other Enzymes	AChE, acetylcholinesterase	1eve	0.00
Nuclear Hormone Receptors	AR, androgen receptor	1xq2	0.00
Kinases	PDGFrb, platelet derived growth factor receptor kinase	N/A	0.00
Kinases	HSP90, human heat shock protein 90	1uy6	0.00
Kinases	CDK2, cyclindependent kinase 2	1ckp	0.00
Metalloenzymes	COMT, catechol O-methyltransferase	1h1d	0.00
Other Enzymes	HIVRT, HIV reverse transcriptase	1rt1	0.00
Serine Proteases	FXa, factor Xa	1f0r	0.00
Kinases	EGFr, epidermal growth factor receptor	1m17	0.00