Drug design is a critical step in the drug discovery process, where promising drug molecules are engineered to be later evaluated preclinically and perhaps clinically. Phenotypic drug design has again gained traction. Cancer cell lines, a frequently adopted in vitro model for phenotype drug design, can be used to evaluate the drug resistance level (lack of inhibitory activity, for example) of a large number of molecules, and discard those that are the least likely to become drug candidates. By reusing these datasets, supervised learning models have been built to predict drug resistance on cancer cell lines. Usually, these methods have assigned reliability to the whole model rather than reliability to individual predictions (molecules). In problems such as drug design, accurately achieving the latter would revolutionize decision making. Conformal prediction is a model-agnostic method to assign reliability to each model prediction. In this study, we investigated the impact of conformal prediction on the prediction of inhibitory activity of molecules on a given cancer cell line. This analysis was carried out in each of the 60 cell lines from the NCI-60 panel to understand the variability of the results across cancer types. We also discussed the implications of predicting the molecules considered most potent. In addition, we investigated how the further subdivision of the training set to build conformal prediction models may affect the results obtained. Overall, we observed that those molecules deemed most reliable by conformal prediction are substantially better predicted than those that are not. This suggest that such computational tools are promising to guide phenotypic drug design.

The directories of this repository contain the following information: - CP_NCI60_data: Data needed, in .feather format, to recreate the main results of this paper. - CPModels: Saved models in .pkl format. - python_code: Scripts to generate the main results of this paper. See the README.MD for more information.