Prediction of complexation properties of crown ethers using computational neural networks

Journal of Inclusion Phenomena and Molecular Recognition in Chemistry

Volumn 27, Issue 3, 1997, Pages 201-213

  Gakh, Andrei A ^a   Sumpter, Bobby G ^a   Noid, Donald W ^a   Sachleben, Richard A ^a   Moyer, Bruce A ^a  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

Author keywords

Complexes; Computational neural networks; Crown ethers; Stability constants; Structure property relationships

Indexed keywords

EID: 0031521350     PISSN: 09230750     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007928814162     Document Type: Article

References (24)

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20. 20. Our preliminary computations indicate that the influence of temperature and solvent can be incorporated in neural network computing by adding two additional inputs (temperature and solvent) in the neural network architecture. For a limited set of solvents (for example, methanol, acetonitrile, water) they can be coded sequentially, 1,2, and 3. However, for a large set of solvents a more sophisticated coding algorithm is required. These computations lie beyond the scope of the current work.
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