The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a ‘one-target fixation’ to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.

众所周知，药物的治疗作用是由于它们与多个细胞内靶标的相互作用而产生的。因此，制药行业目前正从基于“单目标固定”的还原论方法转变为整体的多目标方法。但是，许多药物发现实践仍是程序性抽象，其原因是试图了解并解决生物活性化合物的作用，同时防止不良反应。在这里，我们讨论药物发现如何从进化生物学原理中受益，并报告两个现实生活中的案例研究。我们通过关注可取性原则及其许多功能和应用（例如基于机器学习的多准则虚拟筛选）来做到这一点。