a) I. I. Mechnikov National University, Chemistry Department, Dvorianskaya 2, Odessa 65026, Ukraine, e-mail alhimikir@gmail.com b) Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukrainec) Badger Technical Services, LLC, Vicksburg, Mississippi, USAd) Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Jackson State University, Jackson, Mississippi, 39217, USA

TWO-LAYER QSPR MODEL FOR PREDICTION OF ORGANIC COMPOUNDS

A QUEOUS SOLUBILITY AT VARIOUS TEMPERATURES

2013

Presented by:Klimenko K.

Odessa national universityChemistry department

Challenges of aqueous solubility determination

Other factors which can effect solubility

1.Pressure2.Solution equilibrium 3.pH4.State of substance5.Methods for excessive solute removal

These factors are frequently not taken to the account when solubility determination is carried out. Moreover, there is no universally recognized method for the experiment, therefore, solubility data can be variegated.

3

Temperature-solubility relationship

constdTdSol

Example

solubility temperature coefficient(kj)

4

Assessment of regression equation fit5

Two-layer QSPR approach for aqueous solubility model development

Molecular descriptors QSPR of aqueous

solubility at 25 oC (lg(xj)25)

Aqueous solubility prediction in range

0<t<100

lg(xj)t = f (lg(xj)25, kj, t)

 

QSPR of solubility

temperature coefficient (kj)

6

Feature net procedure for QSPR solubility model developmentSolubility temperature coefficient (kj) calculation from experimental data

QSPR model for coefficient prediction (kj) Generating Simplex descriptors

QSPR solubility model 0<t<100 0C

7

Prediction of (kj) value for all compounds in the set

Calculation of descriptor kj(t-25), for temperature factor impact implementation

Statistical characteristics of QSPR models for solubility temperature coefficients

8

T1 T2 T3 T4 T5 Average

n 65 65 65 65 65

Variable number 50 70 50 50 70

Tree number 150 150 150 150 250

R2 0.97 0.98 0.97 0.97 0.97 0.97

R2test

0.81 0.61 0.85 0.83 0.81 0.78

R2(oob)

0.75 0.76 0.77 0.74 0.72 0.75

S(ws)0.0027 0.0022 0.0027 0.0025 0.0028 0.0026

S(oob)0.0073 0.0062 0.0072 0.0072 0.0075 0.0071

S(ts)0.0067 0.0095 0.0053 0.0058 0.0038 0.0062

n – number of data points T(1-5) – test sets

Obs. vs Pred. solubility coefficient plot9

Statistical characteristics of feature net QSPR models for solubility at temperature range 0>t>100 0C

T1 T2 T3 T4 T5 Average

m 548 548 548 548 548

n 1484 1484 1484 1484 1484

Variable number 200 200 200 200 200

Tree number 150 150 150 150 150

R2 0.99 0.99 0.99 0.99 0.99 0.99

R2test 0.97 0.97 0.97 0.96 0.97 0.97

R2(oob) 0.96 0.96 0.96 0.97 0.96 0.96

S(ws) 0.22 0.22 0.40 0.21 0.21 0.25

S(oob) 0.42 0.47 0.42 0.42 0.42 0.42

S(ts) 0.38 0.35 0.21 0.41 0.34 0.34

m – number of compounds

10

Obs. vs Pred. solubility model plot11

Distribution of prediction error for compounds with various molecular mass

12

Physicochemical parameters' relative influence on solubility in general model

13

Prediction of aqueous solubility for compounds from external test set(t=25,m=28)

14

Compounds name obs. pred.Compounds name obs. pred.

acebutolol -2.67 -2.51pyrimethamine -4.11 -3.22

Amoxicillin -1.97 -2.20salicylic acid -1.93 -1.71

trazodone -3.10 -2.74sulfamerazine -3.12 -2.65

folic acid -5.25 -2.59sulfamethizole -2.78 -2.66

furosemide -4.23 -3.08terfenadine -7.74 -8.59

hydrochlorothiazide -2.68 -2.63thiabendazole -3.48 -2.97

imipramine -4.10 -4.41tolbutamide -3.46 -2.69

indometacin -2.94 -4.84Benzocaine -2.33 -2.02

ketoprofen -3.21 -4.11benzthiazide -4.83 -4.40lidocaine -1.87 -2.28clozapin -3.24 -4.16

meclofenamic acid -6.27 -4.51dibucaine -4.39 -3.76

naphthoic acid -3.77 -3.47diethylstilbestrol -4.43 -4.96

Bendroflumethiazide -4.30 -3.57diflunisal -4.46 -4.20

probenecid -4.86 -2.98dipyridamole -5.16 -2.54

model 1/T two-layer feature netS 3,57 1,39 1,18% accurate predictions 17,9 42,9 46,4

Prediction of aqueous solubility at different temperatures

OP

O

CH3CH3

Cl

OHO

98634-28-7

t= 20-40 oCt= 15-55 oC

87-69-4

O O

O

H3C

CH3

O

O

NH2

H3C CH3

O

OH

OH

OH

O

OH

O O OCH3

H3C CH3

75885-58-4

t= 22-63 oC

t= 15-65 oC

484-12-8

482-44-0

t= 15-55 oC

m=5,k=35%acc.pred.comp=75%acc.pred.data points=71,4

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Conclusion

- SiRMS allows developing QSPR models for successful aqueous solubility in temperature range 0-100 оС.

- Linear regression equation is the best to describe solubility logarithm dependence on temperature. It is also useful for defining solubility temperature coefficient.

- Electrostatics (25%) and lipophilicity (18%) have max impact on solubility. Temperature factor’s influence is also substantial and equals 3%.

- Information derived from 2D-structure is sufficient for aqueous solubility prediction.

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Thank you for your attention!