Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound's tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/andhttp://www.ncbi.nlm.nih.gov/projects/geo (GPL96, GSE32474).