Volume learning algorithm artificial neural networks for 3D QSAR studies.

I V Tetko; V V Kovalishyn; D J Livingstone

doi:10.1021/jm010858e

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Volume learning algorithm artificial neural networks for 3D QSAR studies.

I V Tetko·V V Kovalishyn·D J Livingstone

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Abstract

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.

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Algorithms Cannabinoids Drug Design Indoles Models, Molecular Molecular Mimicry Neural Networks, Computer Quantitative Structure-Activity Relationship Journal Article Research Support, Non-U.S. Gov't

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I V Tetko, V V Kovalishyn, D J Livingstone. 2001-07-19. Volume learning algorithm artificial neural networks for 3D QSAR studies.. https://doi.org/10.1021/jm010858e

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