Abstract
QSAR (Quantitative Structure-Activity Relation-ship) modelling is one of the well developed areas in drug development through computational chemistry. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR modelling. Machine learning algorithms are important tools for QSAR analysis, as a result, they are integrated into the drug production process. In this paper we will try to go through the problem of learning the Complex-Valued Neural Networks(CVNNs) using Particle Swarm Optimiza-tion(PSO); which is one of the open topics in the machine learning society where the CVNN is a more complicated for complex-valued data processing due to a lot of constraints such as activation function must be bounded and differentiable at the complete complex space. In this paper, a CVNN model for real-valued regression problems s presented. We tested such trained CVNN on two drug sets as a real world benchmark problem. The results show that the prediction and generalization abilities of CVNNs is superior in comparison to the conventional real-valued neural networks (RVNNs). Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.
Highlights
The problem of drug design is to find drug candidates from a large collection of compounds that will bind to a target molecule of interest
The major bottlenecks in drug discovery ware addressed with computer-assisted methods, such as QSAR models [4], where the molecular activities are critical for drug design
We propose a new strategy for training the complexcomplex-valued neural networks (CVNNs) using PSO in QSAR modelling for drug design
Summary
The problem of drug design is to find drug candidates from a large collection of compounds that will bind to a target molecule of interest. Machine learning algorithms have been used in the modelling of QSAR problems [5], [6], [7] They extract information from experimental data by computational and statistical methods and generate a set of rules, functions or procedures that allow them to predict the properties of novel objects that are not included in the learning set. The machine learning field [9], [10], [11] have versatile methods or algorithms such as decision trees (DT), lazy learning, k-nearest neighbours, Bayesian methods, Gaussian processes, artificial neural networks (ANN), support vector machines (SVM), and kernel algorithms for a variety of tasks in drug design These methods are alternatives to obtain satisfying models by training on a data set.
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