ALOGPS is a free on-line program to predict lipophilicity and aqueous solubility of chemical compounds

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1 ALOGPS is a free on-line program to predict lipophilicity and aqueous solubility of chemical compounds Igor V. Tetko & Vsevolod Yu. Tanchuk IBPC, Ukrainian Academy of Sciences, Kyiv, Ukraine and Institute for Bioinformatics, Munich, Germany March 5th, ACS

2 ALOGPS 2. LogP: 75 input variables corresponding to electronic and topological properties of atoms (E-state indices), 298 molecules in the database, 64 neural networks in the ensemble. Calculated results RMSE=.35, MAE=.26, n=76 outliers (>.5 log units) LogS: 33 input E-state indices, 29 molecules in the database, 64 neural networks in the ensemble. Calculated results RMSE=.49, MAE=.35, n=8 outliers (>.5 log units) Tetko, Tanchuk & Villa, JCICS, 2, 4, Tetko, Tanchuk, Kasheva & Villa, JCICS, 2, 4, Tetko & Tanchuk, JCICS, 22, 42,

3 Representation of molecules SMILES (no stereoisomers) H, A -- number of hydrogen and non-hydrogen atoms E-state indexes developed by Kier & Hall 7 Cl Basic atom-type E-state indexes Extended atom-type Bond-type 2 O HO H O 4 O 3 Advantages to use atom-type E-state indices: o missed fragments! ==> non-linear dependencies indices/property Atom type E-state: SdO, SsOH,SsCl, Extended: SdO(nitro), SdO(acid), Bond-type: e2o2, eac3c3aa,ec3cla,...

4 MLRA vs eural etwork method indices R 2 RMSE MLRA 75 E-state indices, including A, H.89.6 AS the same logp=2.65 logp=5. logp=2.28 Cl logp=.2 Ssss logp=5.8

5 Prediction of AstraZeneca logp set n=2569 ACDlogP (v. 7.): MAE =.86, RMSE=.2 CLOGP (v. 4.7): MAE =.7, RMSE=.7 ALOGPS: MAE =.6, RMSE=.84 Tetko & Bruneau, J. Pharm. Sci., 24, 94, 33-3.

6 ALOGPS: Extrapolation vs Interpolation BASF, n=6 AstraZeneca, n=2569 Tetko, JCICS, 22, 42, Tetko & Bruneau, J. Pharm. Sci., 24, 94, 33-3.

7 Prediction Space of the model does not cover the in house compounds Training set data In house data

8 Possible strategies Generate new indices and build a new model --method is used by fragment-based approaches (ACDLabs, PharmaAlgorithms) provides an improvement but may have danger of overfitting that can lower prediction ability Do not generate new indices/model but to extend the model into the uncovered space and correct the model using k-- no danger of overfitting (Associative eural etwork)

9 Training set model Interpolation

10 Prediction of the test compounds extrapolation

11 ew data prediction

12 k- correction ! We can make an adjustment of the predicted value by identifying the nearest neighbors () of the analyzed data case How to detect the nearest neighbors? Why we need for this ensemble of models?

13 earest neighbors for Gauss function A y B y x x Detection of nearest neighbors in space of models make us of invariants structureproperty and it is much more accurate than in the initial space of parameters C x x Y=f(x +x 2 ) x

14 Early Stopping Over Ensemble (ESE) Learning/Validation Sets Artificial eural etwork Training Set no network no Artificial eural etwork Ensemble Analysis Set no 2 network no 2 Learning Partition InitialTraining Set Set no 99 network no 99 Validation Set no 2 network no 2

15 AS: an example correction logp=3. HO logp=3.48 HO " 2.3% $ $ 4.6 $ M $ $ 3.2 # $.& " 3.7% $ $ 4.8 $ M $ $ 5.8 # $ 2.& " net % $ $ net 2 $ M $ $ net 63 # $ net 64& " net % $ $ net 2 $ M $ $ net 63 # $ net 64& -k correction Morphinan-3-ol, 7-methyl- Calculated logp=3.65, δ= > =2.89 (δ=+.22) Levallorphan Calculated logp=4.24, δ= > =3.7 (δ=+.22) -- both molecules are the nearest neighbors, r 2 =.47, in space of residuals!

16 Associative eural etwork (AS) A prediction of case i: [ x i ] [ AE] M = [ z i ] = " $ $ $ $ $ # $ z i M z k i M z M i % & Ensemble approach: z =! i i z k M k =, M Pearson s (Spearman) correlation coefficient r ij =R(z i,z j )> in space of residuals z$ i = z i + k j"!( y # ) j z j k ( x i ) <<= AS bias correction The correction of neural network ensemble value is performed using errors (biases) calculated for the neighbor cases of analyzed case x i detected in space of neural network models

17 earest neighbors for Gauss function A y B y x x C x 2 4 Detection of nearest neighbors in space of models make us of invariants structureproperty and it is much more accurate than in the initial space of parameters x x

18 Gauss function extrapolation A y B y.8.8 Advantages: fast, no neural network retraining; correction is not limited by the range of values in the training set. C y x D y x otice: y=f(x=x +x 2 ) x

19 Property-based clustering A logp Pearsons linear correlation coefficient, R 3.3 (.53) aphthalene B -.26 (.94) Pyrazine A: lipophilicity prediction Cl 2.84 (.58) Phenyl Cloride -.4 (.94) Pyrimidine B: molecular weight prediction O 2. (.59) Anisole 2.73 (.6) Toluene 2.73 (.94) Toluene.65 (.97) Pyridine 2.3 Benzene C D Tetko, JCICS, 22, 42,

20 ALOGPS: Extrapolation vs Interpolation ALOGPS logp (blind) :MAE =.27, RMSE=.63 ALOGPS logp (LIBRARY):MAE =.49, RMSE=.7 Tetko, JCICS, 22, 42, Tetko & Bruneau, J. Pharm. Sci., 24, 94, 33-3.

21 Function training and real

22 new points are measured "LogD".75 training "LogP"

23 Library mode (no retraining) Library "LogD" "LogD".75 training "LogP"

24 Library mode vs training using cases

25 Analysis of Pfizer data Pallas PrologD : ACDlogD (v. 7.9): ALOGPS: ALOGPS LIBRARY: MAE =.6, RMSE=.4 MAE =.97, RMSE=.32 MAE =.92, RMSE=.7 MAE =.43, RMSE=.64 Tetko & Poda, J. Med. Chem., 24, 94,

26 PHYSPROP data set Total: 298 nova set star set CLOGP training nova --> prediction star set XLOGP 873 PHYSPROP 2 98

coefficient of a test compound to training set compounds MAE=.

27 Prediction performance as function of similarity in space of star set models Blind prediction LIBRARY mode max correlation coefficient of a test compound to training set compounds MAE=.43 max correlation coefficient of a test compound to LIBRARY compounds MAE=.28 (.26)

28 Reliability of new compound predictions r error >.7 ~.6 ~.5 ~.4 <.3 CI, 25, 2, 65, PHYSPROP

29 Reliability of new compound predictions r error >.7 ~.6 ~.5 ~.4 <.3 CI, 25, 2, 65, Aurora data PHYSPROP

30 Aqueous solubility / logp prediction for Pfizer data Towards Predictive ADME Profiling of Drug Candidates: Lipophilicity and Solubility Gennadiy Poda, Igor Tetko and Douglas C. Rohrer MEDI Wednesday, March 6th

31 Conclusion The LIBRARY mode significantly improves prediction for in house logp/s and logd data sets The LIBRARY mode can be used with very small number of compounds, i.e one. This number will not be adequate to create new model from scratch The improvement in this mode due to presence of invariants conserved both for training and test sets The LIBRARY mode can be used for non-stationary and contradiction data An apparent success of logd prediction suggest that similar indices dominates in logp and pka properties

32 Acknowledgement Part of this presentation was done thanks to Virtual Computational Chemistry Laboratory ITAS-IFO project ( I thank Prof Hugo Kubinyyi, Drs Pierre Bruneau and Gennadiy Poda for collaboration and Prof. Tudor Oprea for inviting me to participate in this conference. Thank you for your attention!

33 Free on-line/batch analysis on

Encoding molecular structures as ranks of models: A new, secure way for sharing chemical data and development of ADME/T models. Igor V.

Encoding molecular structures as ranks of models: A new, secure way for sharing chemical data and development of ADME/T models Igor V. Tetko IBPC, Ukrainian Academy of Sciences, Kyiv, Ukraine and Institute