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Progress in Biophysics and Molecular
Biology

Volume
70, Issue 3, November 1998, Pages 175–222

Artificial neural networks for computer-based molecular
design

Gisbert
Schneidera,
,

Paul Wredeb

a F. Hoffmann-La Roche Ltd., Pharmaceuticals
Division, Molecular Design and Bioinformatics, CH-4070 Basel,
Switzerland

b Freie Universität Berlin,
Universitätsklinikum Benjamin Franklin, Institut für Medizinische/Technische
Physik und Lasermedizin, AG Molekulare Bioinformatik, Krahmerstraße 6–10,
D-12207 Berlin, Germany

Available online 4 January 1999

http://dx.doi.org.proxy.kobson.nb.rs:2048/10.1016/S0079-6107(98)00026-1,
How to Cite or Link Using DOI

Abstract

The theory of artificial neural networks is briefly reviewed focusing on
supervised and unsupervised techniques which have great impact on current
chemical applications. An introduction to molecular descriptors and
representation schemes is given. In addition, worked examples of recent advances
in this field are highlighted and pioneering publications are discussed.
Applications of several types of artificial neural networks to compound
classification, modelling of structure–activity relationships, biological target
identification, and feature extraction from biopolymers are presented and
compared to other techniques. Advantages and limitations of neural networks for
computer-aided molecular design and sequence analysis are discussed.

Keywords

Combinatorial chemistry;

Compound library;

Drug design;

Genetic algorithm;

Molecular diversity;

Molecular descriptor;

Neural network;

Sequence analysis;

Structure–activity relationship

Abbreviations

2D, two-dimensional;

3D, three-dimensional;

ANN, artificial neural networks;

ART, adaptive resonance theory;

BAM, bi-directional associative memory;

bp, back-propagation-of-errors;

CAMD, computer-aided molecular design;

CPN, counter-propagation network;

LMS, least-mean-square;

PCA, principle component analysis;

QSAR, quantitative structure–activity relationship;

RBF, radial basis function;

ReNDeR, reversible non-linear dimension reduction;

SAR, structure–activity relationship;

SBB, structural building blocks;

SNN, supervised neural networks;

UNN, unsupervised neural networks

Figures and tables from this article:

Fig. 1. (a) Data distribution revealing two
major clusters; (b) modelling a mathematical function f(x)
to establish an input-output relationship by SNN; (c) a classificator (line)
formed by a SNN separating two classes of data (shown as circles and squares).

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in workspace

Fig. 2. Different architectures of feed-forward
networks. Formal neurons are drawn as circles, weights are represented by
lines connecting the neurons. The flow of information through the networks is
from top to bottom. White circles: fan-out neurons; gray circles: sigmoidal
neurons; black circles: linear neurons. (a) Perceptron; (b) three-layered
feed-forward network; (c+d) two-layered feed-forward
network.

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in workspace

Fig. 3. Principle of vector quantization by
unsupervised network training. The network weight vectors (filled arrowheads)
move towards centers of the data distribution during training. Location of the
weight vectors before (a) and after training (b). Data points are drawn as
vectors with open arrowheads. Here two clusters are formed by two neurons; ×1
and ×2 are two dimensions of the data space.

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in workspace

Fig. 4. Examples of topology-preservation in
Kohonen-networks. (a) training of a 10×10 Kohonen-network with randomly
distributed data; (b) training of a 10×10 Kohonen-network with only a few data
points roughly forming the shape of a heart. Stages of network development
during the training process are shown. At the end of UNN training the network
topologies reflect the underlying data distributions.

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in workspace

Fig. 5. Architecture of an encoder network
(ReNDeR) mapping a seven-dimensional input to two dimensions. White circles:
fan-out neurons; gray circles: sigmoidal neurons; black circles: linear
neurons. After network training the data projection onto a plane can be
displayed by plotting the output of the central neurons.

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