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Quantum Machine Learning Quantum Chemistry

Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network.

PubMed
Authors: Gini G, Lorenzini M, Benfenati E, Grasso P, Bruschi M

Year

1999

Paper ID

13420

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

95

Citations

N/A

Abstract

A back-propagation neural network to predict the carcinogenicity of aromatic nitrogen compounds was developed. The inputs were molecular descriptors of different types: electrostatic, topological, quantum-chemical, physicochemical, etc. For the output the index TD50 as introduced by Gold and colleagues was used, giving a continuous numerical parameter expressing carcinogenicity. From the tens of descriptors calculated, principal component analysis enabled us to restrict the number of parameters to be used for the artificial neural network (ANN). We used 104 molecules for the study. An Rcv2 = 0.69 was obtained. After removal of 12 outliers, a new ANN gave an Rcv2 of 0.82.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 1999 reference point for readers tracking recent quantum research.
  • A back-propagation neural network to predict the carcinogenicity of aromatic nitrogen compounds was developed.

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References & Citation Signals

[1] DOI https://doi.org/10.1021/ci9903096 [2] Publisher https://pubmed.ncbi.nlm.nih.gov/10614025/

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