Applications of Artificial Neural Network Modeling in Drug Discovery

Abstract

Artificial Neural Networks (ANNs) modeling is a group of computer algorithms for modeling and pattern recognition, functioning similarly to the neurons of the brain. The brain learns from its experience. In the brain, a biological neuron receives inputs from many external resources, combines them, performs a non-linear operation, and then makes a decision based on the final results. The ANNs are a type of mathematical model that simulates the biological nervous system and draws on analogues of adaptive biological neurons. A major advantage of ANNs compared to statistical modeling is that they do not require rigidly structured experimental designs and can map functions using historical or incomplete data. ANNs are good recognizers of patterns and robust classifiers, with the ability to generate when making decisions based on imprecise input data.ANNs are known to be a powerful tool to simulate various nonlinear systems and have been applied to numerous problems of considerable complexity in many field including pharmaceutical research, engineering, psychology and medicinal chemistry.The potential applications of ANN methodology in the pharmaceutical sciences are broad. In this paper, we will review some applications of ANNs in drug discovery. Quantitative Structure-Activity Relationship (QSAR)QSAR correlates physicochemical parameters of compounds with chemical or biological activities. These parameters include topological parameters, molecular weight, molar volume, electronegativity, logP, hydrogen acceptor, hydrogen donor and molar refractivity. ANNs have been shown to be an effective tool to establish this type of relationship and predict the activities of new compounds. Jaen-Oltra et al.[1] developed a new topological method to predict antimicrobial properties of quinolones derivatives on the basis of their chemical structures. An ANN with suitable set of topological descriptors and training algorithms was used to determine the minimum inhibitory concentration of quinolones. In another example, Hu et al. [2] combined ANNs, quantum chemistry and molecular docking methods to design novel Aldose Reductase Inhibitors (ARIs). Physicochemical parameters including electronegativity and molar volume of known inhibitors were calculated using quantum chemistry methods. Using these parameters as input nodes, an ANN based QSAR model was constructed and the biological activities of new compounds were predicted. The further molecular docking analysis showed all the predicted potent compounds by ANNs binded well to the active site of the aldose reductase. Virtual Screening (VS)Virtual screening is a novel approach to speed the drug discovery process. It applies computational methods to quickly search (or "screen") compounds with known chemical structures to identify the compounds with high biological activities in a compound database. ANNs-based QSAR models are widely chosen as the prediction methods in the virtual screening. Recently, Myint et al. [3] have developed a 2D fingerprint-based artificial neural network QSAR (FA...

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