Computational Prediction of Chemical Tools for Identification and Validation of Synthetic Lethal Interaction Networks

Cancer is one of the leading causes of death and chromosomal instability (CIN) is a hallmark feature of cancer. CIN, a source of genetic variation in either altered chromosome number or structure contributes to tumor heterogeneity and has become a hot topic in recent years prominently for its role in therapeutic responses. Synthetic lethality and synthetic rescue based approaches, for example, advancing CRISPR-Cas9 platform, are emerging as a powerful strategy to identify new potential targets to selectively eradicate cancer cells. Unfortunately, only few of them are further explored therapeutically due to the difficulty in linking these targets to small molecules for pharmacological intervention. This, however, can be alleviated by the efforts to bring chemical, bioactivity, and genomic data together, as well as established computational approaches. In this chapter, we will discuss some of these advances, including established databases and in silico target-ligand prediction, with the aim to navigate through the synthetically available chemical space to the biologically targetable landscape, and eventually, to the chemical modeling of synthetic lethality and synthetic rescue interactions, that are of great clinical and pharmaceutical relevance and significance.