  Personalized cancer treatment requires a thorough understanding of complex
interactions between drugs and cancer cell lines in varying genetic and
molecular contexts. To address this, high-throughput screening has been used to
generate large-scale drug response data, facilitating data-driven computational
models. Such models can capture complex drug-cell line interactions across
various contexts in a fully data-driven manner. However, accurately
prioritizing the most sensitive drugs for each cell line still remains a
significant challenge. To address this, we developed neural ranking approaches
that leverage large-scale drug response data across multiple cell lines from
diverse cancer types. Unlike existing approaches that primarily utilize
regression and classification techniques for drug response prediction, we
formulated the objective of drug selection and prioritization as a drug ranking
problem. In this work, we proposed two neural listwise ranking methods that
learn latent representations of drugs and cell lines, and then use those
representations to score drugs in each cell line via a learnable scoring
function. Specifically, we developed a neural listwise ranking method,
List-One, on top of the existing method ListNet. Additionally, we proposed a
novel listwise ranking method, List-All, that focuses on all the sensitive
drugs instead of the top sensitive drug, unlike List-One. Our results
demonstrate that List-All outperforms the best baseline with significant
improvements of as much as 8.6% in hit@20 across 50% test cell lines.
Furthermore, our analyses suggest that the learned latent spaces from our
proposed methods demonstrate informative clustering structures and capture
relevant underlying biological features. Moreover, our comprehensive empirical
evaluation provides a thorough and objective comparison of the performance of
different methods (including our proposed ones).
