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Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks

Authorized Users Only
2012
Authors
Erić, Slavica
Kalinić, Marko
Popović, Aleksandar
Zloh, Mire
Kuzmanovski, Igor
Article (Published version)
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Abstract
In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n = 280) and test (n = 94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEPtrain = 0.668) and test set (RMSEPtest = 0.679). The model was found to be a highly in...terpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.

Keywords:
Prediction of solubility / Artificial neural network / Relative importance of descriptors
Source:
International Journal of Pharmaceutics, 2012, 437, 1-2, 232-241
Publisher:
  • Elsevier Science BV, Amsterdam
Funding / projects:
  • Computational design, synthesis and biological evaluation of new heterocyclic compounds as selective tumorogenesis inhibitors (RS-172009)

DOI: 10.1016/j.ijpharm.2012.08.022

ISSN: 0378-5173

PubMed: 22940210

WoS: 000309115300026

Scopus: 2-s2.0-84866731321
[ Google Scholar ]
16
13
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/1644
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Erić, Slavica
AU  - Kalinić, Marko
AU  - Popović, Aleksandar
AU  - Zloh, Mire
AU  - Kuzmanovski, Igor
PY  - 2012
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1644
AB  - In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n = 280) and test (n = 94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEPtrain = 0.668) and test set (RMSEPtest = 0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.
PB  - Elsevier Science BV, Amsterdam
T2  - International Journal of Pharmaceutics
T1  - Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks
VL  - 437
IS  - 1-2
SP  - 232
EP  - 241
DO  - 10.1016/j.ijpharm.2012.08.022
ER  - 
@article{
author = "Erić, Slavica and Kalinić, Marko and Popović, Aleksandar and Zloh, Mire and Kuzmanovski, Igor",
year = "2012",
abstract = "In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n = 280) and test (n = 94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEPtrain = 0.668) and test set (RMSEPtest = 0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "International Journal of Pharmaceutics",
title = "Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks",
volume = "437",
number = "1-2",
pages = "232-241",
doi = "10.1016/j.ijpharm.2012.08.022"
}
Erić, S., Kalinić, M., Popović, A., Zloh, M.,& Kuzmanovski, I.. (2012). Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks. in International Journal of Pharmaceutics
Elsevier Science BV, Amsterdam., 437(1-2), 232-241.
https://doi.org/10.1016/j.ijpharm.2012.08.022
Erić S, Kalinić M, Popović A, Zloh M, Kuzmanovski I. Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks. in International Journal of Pharmaceutics. 2012;437(1-2):232-241.
doi:10.1016/j.ijpharm.2012.08.022 .
Erić, Slavica, Kalinić, Marko, Popović, Aleksandar, Zloh, Mire, Kuzmanovski, Igor, "Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks" in International Journal of Pharmaceutics, 437, no. 1-2 (2012):232-241,
https://doi.org/10.1016/j.ijpharm.2012.08.022 . .

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