@article { ISI:000341643300008,
	title = {Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method},
	journal = {Letters in Drug Design \& Discovery},
	volume = {11},
	number = {9},
	year = {2014},
	month = {NOV},
	pages = {1112-1118},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.</p>},
	keywords = {ADME modeling, Caco-2 cell permeability, genetic programming, MLP, SVR},
	issn = {1570-1808},
	author = {Vyas, Renu and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, S. S. and Kulkarni, B. D.}
}
@article { ISI:000335400800021,
	title = {Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms},
	journal = {Bioenergy Research},
	volume = {7},
	number = {2},
	year = {2014},
	month = {JUN},
	pages = {681-692},
	publisher = {SPRINGER},
	address = {233 SPRING ST, NEW YORK, NY 10013 USA},
	abstract = {<p>The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (\&gt; 0.95) magnitudes of the coefficient of correlation and low (\&lt; 4.5 \%) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.</p>},
	keywords = {Artificial intelligence, Artificial neural networks, Biomass fuels, genetic programming, Higher heating value, Multilayer perceptron},
	issn = {1939-1234},
	doi = {10.1007/s12155-013-9393-5},
	author = {Ghugare, Suhas B. and Tiwary, S. and Elangovan, V. and Tambe, S. S.}
}
