A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors.
A novel approach that combines neural networks, computer docking and quantum mechanical method is developed to design potent aldose reductase inhibitors (ARIs). Neural networks is employed to determine the quantitative structure-activity relationship (QSAR) among the known ARIs. The physical descriptors of the neural networks, such as electronegativity and molar volume, are evaluated with first-principles quantum mechanical method. Based on the QSAR, new candidates for ARI are predicted, and subsequently screened via computer docking technique. The surviving candidates are further tested via quantum mechanical calculation for their bindings to aldose reductase. We find that the best 49 predicted ARI candidates have better calculated binding energies than those of experimentally known drug candidates.
Hu
LiHong
L
Department of Chemistry, The University of Hong Kong, Pokfulam, HKSAR, Hong Kong, China.
Chen
GuanHua
G
Chau
Raymond Ming-Wah
RM
D000449:Aldehyde Reductase / Q000037:antagonists & inhibitors* / Q000378:metabolism; D001665:Binding Sites; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D020128:Inhibitory Concentration 50; D008024:Ligands; D008958:Models, Molecular; D015394:Molecular Structure; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary; D021281:Quantitative Structure-Activity Relationship; D013816:Thermodynamics
D004791:Enzyme Inhibitors; D008024:Ligands; D000449:Aldehyde Reductase