@article { ISI:000334141800011,
	title = {Soft-sensor development for biochemical systems using genetic programming},
	journal = {Biochemical Engineering Journal},
	volume = {85},
	year = {2014},
	month = {APR},
	pages = {89-100},
	publisher = {ELSEVIER SCIENCE BV},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {<p>Soft-sensors are software based process monitoring systems/models. In real-time they estimate those process variables, which are difficult to measure online or whose measurement by analytical procedures is tedious and time-consuming. In this study, the genetic programming (GP), an artificial intelligence based data-driven modeling formalism, has been introduced for the development of soft-sensors for biochemical processes. The novelty of the GP is that given example input-output data, it searches and optimizes both the form (structure) and parameters of an appropriate linear/nonlinear data-fitting model. In this study, GP-based soft-sensors have been developed for two bioprocesses, namely extracellular production of lipase enzyme and bacterial production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) copolymer. While in case study-I, the soft-sensor predicts the time-dependent lipase activity (U/ml), in case study-II it predicts the amount of accumulated polyhydroxyalkanoates (\% dcw). The prediction and generalization performance of the GP-based soft-sensors was compared with the corresponding multi-layer perceptron (MLP) neural network and support vector regression (SVR) based soft-sensors. This comparison indicates that in the first case study the GP-based soft-sensor with the training and test set correlation coefficient (root-mean-squared-error) magnitudes of \&gt;0.96 (approximate to 0.962 U/ml) has clearly outperformed the two other soft-sensors. In case study-II involving bacterial copolymer production, the GP and SVR based soft-sensors have performed equally well (correlation coefficient approximate to 0.98) while the MLP based soft-sensor{\textquoteright}s performance was relatively inferior (correlation coefficient approximate to 0.94). (C) 2014 Elsevier B.V. All rights reserved.</p>},
	keywords = {Artificial intelligence, Batch processing, Bioprocess monitoring, Lipase, Modeling, sensors},
	issn = {1369-703X},
	doi = {10.1016/j.bej.2014.02.007},
	author = {Sharma, Suraj and Tambe, Sanjeev S.}
}
@article { ISI:000360934800004,
	title = {Study of applications of machine learning based classification methods for virtual screening of lead molecules},
	journal = {Combinatorial Chemistry \& High Throughput Screening},
	volume = {18},
	number = {7},
	year = {2015},
	month = {AUG},
	pages = {658-672},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree - (DT) and Random Forest - (RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27\% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82 \%) and toxicophoric (70.64 \%). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.</p>},
	keywords = {Anti-anginal, anti-arrythmic, anti-bacterial, anti-convulsant, anti-depressant anti-diabetic, binary QSAR, chemophore, machine learning, pharmacophore, toxicophore},
	issn = {1386-2073},
	doi = {10.2174/1386207318666150703112447},
	author = {Vyas, Renu and Bapat, Sanket and Jain, Esha and Tambe, Sanjeev S. and Karthikeyan, Muthukumarasamy and Kulkarni, Bhaskar D.}
}
