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Related Experiment Videos

Artificial neural networks for computer-based molecular design

G Schneider1, P Wrede

  • 1F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, Basel, Switzerland. gisbert.schneider@roche.com

Progress in Biophysics and Molecular Biology
|November 27, 1998
PubMed
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Artificial neural networks (ANNs) are powerful tools in chemistry, excelling in supervised and unsupervised learning for molecular design and sequence analysis. This review covers their applications, advantages, and limitations in chemical research.

Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Bioinformatics

Background:

  • Artificial neural networks (ANNs) are computational models inspired by biological neural networks.
  • Supervised and unsupervised learning techniques are key components of ANNs with significant chemical applications.
  • Molecular descriptors and representation schemes are crucial for inputting chemical information into ANNs.

Purpose of the Study:

  • To provide a comprehensive review of artificial neural networks in chemical applications.
  • To highlight recent advances and discuss pioneering publications in the field.
  • To compare ANN applications with other techniques in areas like molecular design and sequence analysis.

Main Methods:

  • Review of theoretical foundations of ANNs, focusing on supervised and unsupervised learning.

Related Experiment Videos

  • Introduction to molecular descriptors and representation schemes.
  • Discussion of worked examples and case studies in various chemical domains.

    • Main Results:

    • ANNs demonstrate significant impact in compound classification, structure-activity relationship modeling, and biological target identification.
    • Feature extraction from biopolymers is effectively achieved using ANNs.
    • Comparison with other techniques highlights the strengths and weaknesses of ANNs.

    Conclusions:

    • Artificial neural networks offer versatile and powerful approaches for computer-aided molecular design and sequence analysis.
    • Understanding the advantages and limitations of ANNs is crucial for their effective implementation in chemistry.
    • The field continues to advance, with ANNs playing an increasingly important role in chemical research and discovery.