Subject: RE: Neural Networks in Chemistry

From: <[email-masked]>
Date: Fri, 15 Apr 94 17:46:48 GMT

Regarding Steven Schafer's request for info on neural nets in chemistry:

The number of papers discussing chemical applications of neural networks is 
approaching 1000.  The Zupan and Gasteiger book suggested by Dr. Wagener is
an excellent source.  Three recent reviews may be faster to acquire than a
book, their refs are given below.  I would suggest a search of CAS-online
for a more complete list.
Good Luck.

*************************************************
TI   Neural networks in chemistry
AU   Gasteiger, Johann; Zupan, Jure
CS   Org.-Chem. Inst., Tech. Univ. Muenchen
LO   Garching W-8046, Germany
SO   Angew. Chem., 105(4), 510-36 (See also Angew. Chem., Int. Ed. Engl.,
     1993, 105(4), 503-27)
PY   1993
LA   Ger
AB   A review with 68 refs. Models that simulate partial functions of the
     human brain were compared computer models designed to also simulate
     brain functions. The role of artificial neurons for classification
     of objects, modeling of functional relationships and storage and
     processing of data was discussed and related to storage and
     processing of chem. information. The applications in anal. of
     spectroscopic data, prediction of reactions, and control of chem.
     processes were discussed.
KW   neuron computer process chem review; artificial intelligence nerve
     process chem; simulation model intelligence nerve process chem
**************************************************
TI   Neural Networks: a new computational paradigm with applications in chemistry
AU   Elrod, David W.; Maggiora, Gerald M.
CS   Upjohn Lab.
LO   Kalamazoo, MI, USA
SO   Proc. Montreux Int. Chem. Inf. Conf., 138-62.  Ed by: Collier, Harry.  Infonortics: Calne, UK.
PY   1993
LA   Eng
AB    A review with 136 references.  Computational neural networks are a new
computational paradigm, inspired by the brain's massively parallel network of
highly interconnected neurons. The interest in computational neural networks
(CNN) derives mainly because they offer a new general paradigm for model
building and function approximation, and not because of their relationship to
models of brain function.  Neural networks are considerably different from
other computational methods in that functional relationships are not programmed
in but are learned directly from data.  This ability to adapt to new data,
along with the "model- free" nature of neural networks allows them to excel at
constructing very general, complex, nonlinear mappings between sets of
input-output pairs, even with noisy data. In fact, CNNs can construct mappings
of arbitrary accuracy for all reasonably well- behaved functions.  In addition
to their mapping ability, neural networks are also useful for pattern
recognition/classification and self-organized clustering. A survey of the
rapidly increasing number of applications of neural networks in chemistry will
highlight some of their properties and limitations, and show what their impact
may be in computer-aided chemistry. 
KW  Computational neural networks, artificial neural networks, review,
   chemical applications. 

*****************************************************
TI   Feed-Forward Neural Networks in Chemistry: Mathematical Systems for 
     Classification and Pattern Recognition
AU   Burns, John A.; Whitesides, George M.
CS   Dept. Chem. Harvard U.
LO   Cambridge, MA. 02138
SO   Chem. Rev., 93(8), 2583-2601
PY   1993
AB   A review with 123 refs.

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    Computational Chemistry         Upjohn Laboratories              
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