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OJBTM

Online Journal of Bioinformatics

 

Volume 21 (3):280-287, 2020.


A model for identifying drug like molecules

 

Kailash Adhikari2, Tapobrata Lahiri1[1], Hrishikesh Mishra1, Kalpana Singh1, Arun Kumar CN2

 

1Indian Institute of Information Technology, Jhalwa Campus, Allahabad, 2IBM India Pvt Ltd., Bangalore-India

 

ABSTRACT

 

Adhikari K, Lahiri T, Mishra H, Singh K, Kumar CAN., A model for identifying drug like molecules, Onl J Bioinform., 21 (3):280-287, 2020. A model to identify drug like characteristics of any small molecule from any database is described. DRAGON software and feature selection was used to extract 15 of 785 sets of features found to be significant for discrimination between drug and non-drug like molecules. These features were fed into a forward back propagation neural network classifier whose weights and biases were optimized through a Neuro-GA module. Selection was based on numerical values. Simple filter of 785 features generated 23% with P > 0.1, 73.8% P < 0.05 and others 0.05 and 0.1 to yield 580 with significant discriminating power. Classification of 600 molecules into drug and non drug like molecules was done on 450 training and 150 as test sets. After training networks we found 88% for training and 86% test sets. We enhanced accuracy for weight and biases by genetic algorithm Neuro-GA, of test set to 90.7%. We confirmed efficiency with 50 new molecules selecting the same 15 features yielding accuracy of 86% and 90%.

 

Key words: Drug likeness, molecular descriptors, data warehousing and mining, backpropagation network.


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