a) I. I. Mechnikov National University, Chemistry Department, Dvorianskaya 2, Odessa 65026, Ukraine, b) Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical- Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine c) Badger Technical Services, LLC, Vicksburg, Mississippi, USA d) 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 university Chemistry department

Challenges of aqueous solubility determination Other factors which can effect solubility 1.Pressure 2.Solution equilibrium 3.pH 4.State of substance 5.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 Example solubility temperature coefficient(k j ) 4

Assessment of regression equation fit 5

Two-layer QSPR approach for aqueous solubility model development Molecular descriptors QSPR of aqueous solubility at 25 o C (lg(x j ) 25 ) Aqueous solubility prediction in range 0<t<100 lg(x j ) t = f (lg(x j ) 25, k j, t) QSPR of solubility temperature coefficient (k j ) 6

Feature net procedure for QSPR solubility model development Solubility temperature coefficient (k j ) calculation from experimental data QSPR model for coefficient prediction (k j ) Generating Simplex descriptors QSPR solubility model 0<t<100 0 C 7 Prediction of (k j ) value for all compounds in the set Calculation of descriptor k j (t-25), for temperature factor impact implementation

Statistical characteristics of QSPR models for solubility temperature coefficients 8 T1T2T3T4T5Average n65 Variable number Tree number R2R R 2 test R 2 (oob) S (ws) S (oob) S (ts) n – number of data points T(1-5) – test sets

Obs. vs Pred. solubility coefficient plot 9

Statistical characteristics of feature net QSPR models for solubility at temperature range 0>t>100 0 C T1T2T3T4T5Average m548 n1484 Variable number200 Tree number150 R2R R 2 test R 2 (oob) S (ws) S (oob) S (ts) m – number of compounds 10

Obs. vs Pred. solubility model plot 11

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 nameobs.pred.Compounds nameobs.pred. acebutolol pyrimethamine Amoxicillin salicylic acid trazodone sulfamerazine folic acid sulfamethizole furosemide terfenadine hydrochlorothiazide thiabendazole imipramine tolbutamide indometacin Benzocaine ketoprofen benzthiazide lidocaine clozapin meclofenamic acid dibucaine naphthoic acid diethylstilbestrol Bendroflumethiazide diflunisal probenecid dipyridamole model1/Ttwo-layerfeature net S3,571,391,18 % accurate predictions17,942,946,4

Prediction of aqueous solubility at different temperatures t= o C t= o C t= o C t= o C t= o C m=5,k=35 %acc.pred.comp=75 %acc.pred.data points=71,4 15

Conclusion -SiRMS allows developing QSPR models for successful aqueous solubility in temperature range о С. -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. 16

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