In parallel to developments in Next-Generation Sequencing for cancer patient therapy decision making, personalized approaches to chemotherapy selection are also becoming desired. In an ideal situation, an individual's genomic, transcriptomic, and tumor-specific in-vitro response to chemical perturbation would be combined, and the US National Cancer Institute NCI-60 project has systematically screened a large chemical library against a variety of cell lines from various tumor types. Therefore, chemoinformatics approaches to make effective use of this data and identify the chemical and biological factors are of value. In this work, we investigate the impact of both chemical and biological descriptions of tumor response to chemical inhibition, and assess how well modeling approaches can predict tumor inhibition response on external datasets. We find that external datasets in both the classification and regression problems are reasonably well addressed, with the impact of chemical description outweighing the contribution from transcriptome or genome descriptions of tumors.