The objective of this study was to develop and investigate an approach to optimally detect, rank, display, and analyze patterns of differential growth inhibition among cultured cell lines. Such patterns of cellular responsiveness are produced by substances tested in vitro against disease-oriented panels of human tumor cell lines in a new anticancer screening model under development by the National Cancer Institute. In the first phase of the study, we developed a key methodological tool, the mean graph, which allowed the transformation of the numerical cell line response data into graphic patterns. These patterns were particularly expressive of differential cell growth inhibition and were conveniently amenable to further analyses by an algorithm we devised and implemented in the COMPARE computer program.

A statistical-heuristic method for selecting drugs for animal screening is developed with molecular structure features as predictors of biological activity. The method is intended to work on large amounts of data over varied structures. A trial of this method on a small data set allows some comparison with more sophisticated pattern recognition methods. Problems connected with interdependence among structure predictors are critical in this method and schemes to eliminate redundancy are reviewed. Alternate sets of structure predictors are considered. The discussion here outlines directions to be taken in the near future.

The National Cancer Institute Division of Cancer Treatment has revised its drug-screening program. About 230,000 compounds in our repository are available for screening under the new protocol. This paper is the first on an attempt to extract a representative sample of these compounds by clustering. It reviews the establishment of the clustering method on a 4980-compound initial sample. The clustering algorithm is fairly simple. However, the molecular fragments employed to match the compounds are somewhat complex to distinguish a large number of compounds.

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