title: Drug-like and non drug-like pattern classification based on simple topology descriptor using hybrid neural network.
abstract: An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.
authors: School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia. wmfahmi@yahoo.com
authors: Wan Mohd Fahmi
authors: WM
authors: Wan-Mamat
authors: Nor Ashidi Mat
authors: NA
authors: Isa
authors: Habibah A
authors: HA
authors: Wahab
authors: Wan Mohd Fairuz
authors: WM
authors: Wan-Mamat
mesh_terms: D000465:Algorithms; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*