ID: ALA3351132

Max Phase: Preclinical

Molecular Formula: C8H14ClNO6

Molecular Weight: 255.65

Molecule Type: Small molecule

Representations

Canonical SMILES:  O=C(CCl)N[C@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O

Standard InChI:  InChI=1S/C8H14ClNO6/c9-1-4(12)10-8-7(15)6(14)5(13)3(2-11)16-8/h3,5-8,11,13-15H,1-2H2,(H,10,12)/t3-,5-,6+,7-,8+/m1/s1

Standard InChI Key:  KYUJZNBOUYNSAF-VDUCJHRSSA-N

Molecule Features

Natural Product: No

Oral: No

Chemical Probe: No

Parenteral: No

Molecule Type: Small molecule

Topical: No

First In Class: No

Black Box: No

Chirality: No

Availability: No

Prodrug: No

Properties

Molecular Weight: 255.65

Molecular Weight (Monoisotopic): 255.0510

AlogP: -2.86

#Rotatable Bonds: 3

Polar Surface Area: 119.25

Molecular Species: NEUTRAL

HBA: 6

HBD: 5

#RO5 Violations: 0

HBA (Lipinski): 7

HBD (Lipinski): 5

#RO5 Violations (Lipinski): 0

CX Acidic pKa: 10.28

CX Basic pKa:

CX LogP: -2.68

CX LogD: -2.68

Aromatic Rings: 0

Heavy Atoms: 16

QED Weighted: 0.35

Np Likeness Score: 1.13

References

1. Pastor M, Cruciani G, Clementi S..  (1997)  Smart region definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships.,  40  (10): [PMID:9154968] [10.1021/jm9608016]

2. Venkatarangan P, Hopfinger AJ..  (1999)  Prediction of ligand-receptor binding thermodynamics by free energy force field three-dimensional quantitative structure-activity relationship analysis: applications to a set of glucose analogue inhibitors of glycogen phosphorylase.,  42  (12): [PMID:10377222] [10.1021/jm980515p]

3. Cruciani G, Watson KA..  (1994)  Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b.,  37  (16): [PMID:8057302] [10.1021/jm00042a012]

4. Pan D, Liu J, Senese C, Hopfinger AJ, Tseng Y..  (2004)  Characterization of a ligand-receptor binding event using receptor-dependent four-dimensional quantitative structure-activity relationship analysis.,  47  (12): [PMID:15163189] [10.1021/jm030586a]

5. Zamora I, Oprea T, Cruciani G, Pastor M, Ungell AL..  (2003)  Surface descriptors for protein-ligand affinity prediction.,  46  (1): [PMID:12502357] [10.1021/jm011051p]

6. Pastor M, Cruciani G, Watson KA..  (1997)  A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure--activity relationship analysis.,  40  (25): [PMID:9406599] [10.1021/jm970273d]

7. Gohlke H, Klebe G..  (2002)  DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein.,  45  (19): [PMID:12213058] [10.1021/jm020808p]

8. So SS, Karplus M..  (1997)  Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications.,  40  (26): [PMID:9435905] [10.1021/jm970488n]

9. Uddin R, Saeed M, Ul-Haq Z.  (2013)  Molecular docking- and genetic algorithm-based approaches to produce robust 3D-QSAR models,  [10.1007/s00044-013-0812-0]

Source

Source(1):

• Scientific Literature