Applications of neural networks in structure-activity relationships of a small number of molecules

• Published in: Journal of medicinal chemistry Vol. 36; no. 7; pp. 811 - 814
• Main Authors: Tetko, I. V, Luik, A. I, Poda, G. I
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 02.04.1993
• Subjects: Biological and medical sciences; Carbazilquinone - analogs & derivatives; General pharmacology; Medical sciences; Mitomycins - pharmacology; Neural Networks (Computer); Pharmacology. Drug treatments; Physicochemical properties. Structure-activity relationships; Structure-Activity Relationship; Statistical analysis; Structure activity relation; Analog; Classification; Prediction; Information processing; Neural network; Conceptual analysis
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm00059a003

We investigated the applications of back propagation artificial neural networks (ANN) for a small dataset analysis in the field of structure-activity relationships. The derivatives of carboquinone were used as an example. It's been found that in this case the use of the same neural network results in unambiguous classification of new molecules. Predictions can be improved with statistical analysis of independent prognosis sets. We suggest that the sign criterion be used as a classification rule. We also compared neural networks with FALS and ALS in leave-one-out prediction. ANN applied to the same dataset has shown the same predictive ability as ALS but poorer than FALS.