Review Biologicals & Immunologicals: Applications of artificial neural networks to quantitative structure-activity relationships

Comparison between neural networks (NN) and principal component analysis (PCA): structure activity relationships of 1,4-dihydropyridine calcium channel antagonists (nifedipine analogues)

The applicability of the neural network computer package PSDD (Perceptron Simulator for Drug Design/Perceptron-type Neural Network Simulator) in structure-activity relationship (SAR) studies was investigated. A group of 1,4-dihydropyridine derivatives was used in order to compare the PSDD results with those obtained previously with PCA. Calculated atomic and molecular descriptors using the semiempirical AM1 method were mainly used. It was shown that the predictive capability demonstrated by PSDD in SAR analysis were almost equivalent to that of PCA.

Volume learning algorithm artificial neural networks for 3D QSAR studies

The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies.

Using artificial neural networks to classify the activity of capsaicin and its analogues

Back-propagation artificial neural networks (ANNs) were trained with parameters derived from different molecular structure representation methods, including topological indices, molecular connectivity, and novel physicochemical descriptors to model the structure--activity relationship of a large series of capsaicin analogues. The ANN QSAR model produced a high level of correlation between the experimental and predicted data. After optimization, using cross-validation and selective pruning techniques, the ANNs predicted the EC50 values of 101 capsaicin analogues, correctly classifying 34 of 41 inactive compounds and 58 of 60 active compounds. These results demonstrate the capability of ANNs for predicting the biological activity of drugs, when trained on an optimal set of input parameters derived from a combination of different molecular structure representations.