Parimal M. Prajapati, Yatri R. Shah, Dhrubo Jyoti Sen
1I. K. Patel College of Pharmaceutical Education and Research, Samarth Campus, Opp. Sabar Dairy, Himmatnagar-383001, Sabarkantha, Gujarat
2Shree H. N. Shukla Institute of Pharmaceutical Education and Research, Behind Marketing Yard, Nr. Lalpari Lake, Amargadh (Bhichari), Rajkot, Gujarat
3Department of Pharmaceutical Chemistry, Shri Sarvajanik Pharmacy College, Hemchandracharya North Gujarat University, Arvind Baug, Mehsana-384001, Gujarat, India,
Volume - 3,
Issue - 1,
Year - 2011
AUTONET, that represents a self training neural network. Results from the neural network are presented visually in order to rapid and easy convey to the medicinal chemist the important features derived by the neural network. The AUTONET approach addresses this through the visual display of the hidden unit weights and thus rapidly conveys useful and informative results to the user. QSAR (Quantitative Structure-Activity Relationships) studies rely heavily upon statistics to derive mathematical models which relate the biological activity of a series of compounds to one or more properties of the molecules. The application of neural networks as a substitute for discriminant analysis. Neural networks in QSAR in a manner similar to multiple regression analysis. Comparative study of neural networks and regression analysis using a set of dihydrofolate reductase inhibitors. Their results indicated neural networks were superior to regression analysis in providing accurate predictions.
Cite this article:
Parimal M. Prajapati, Yatri R. Shah, Dhrubo Jyoti Sen. Artificial Neural Network: A New Approach for QSAR Study. Research J. Science and Tech. 3(1): Jan.-Feb. 2011: 17-24.
Parimal M. Prajapati, Yatri R. Shah, Dhrubo Jyoti Sen. Artificial Neural Network: A New Approach for QSAR Study. Research J. Science and Tech. 3(1): Jan.-Feb. 2011: 17-24. Available on: https://rjstonline.com/AbstractView.aspx?PID=2011-3-1-3