Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

在过去的几十年里，药物发现极大地改善了患者的预后，但一些挑战继续阻碍着新药的快速发展。解决未满足的临床需求需要追求更有可能导致成功药物开发的药物靶点。在这里，我们描述了一种生物信息学方法，通过执行统计分析来确定蛋白质编码基因之间表达水平的定量变化，以及将这些基因分类的共表达网络，从而识别新的癌症药物靶标。随后，我们概述了成药性评估​​方法，以优先考虑和选择要追求的最佳目标。