Fuzzy adaptive least squares and its use in quantitative structure-activity relationships.

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Abstract
Fuzzy adaptive least squares (FALS), a pattern recognition method designed to correlate molecular structure with activity rating, has been developed. A novel feature of FALS is that the degree to which each sample belongs to an activity class is given using a membership function. The algorithm involves an iterative modification of forcing factors to maximize the sum of the membership function values over all samples. This paper first describes the method and calculation procedure of FALS89 (1989 version of FALS), and then shows its application to the correlation of structure with a potency rating of anticarcinogenic mitomycin derivatives and arginine-vasopressin antagonists. FALS89 applied to these samples showed considerably high reliability in both recognition and leave-one-out prediction.

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