Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador.
Facultad de Medicina, Universidad de Las Américas, 170513 Quito, Ecuador.
Drug Discov Today. 2017 Oct;22(10):1489-1502. doi: 10.1016/j.drudis.2017.05.008. Epub 2017 Jun 15.
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.
药物的治疗效果众所周知，是由于它们与多个细胞内靶点相互作用的结果。因此，制药行业目前正在从基于“一个靶点固定”的还原论方法转变为整体多靶点方法。然而，许多药物发现实践仍然是程序性的抽象，源于试图理解和解决生物活性化合物的作用，同时防止不良反应。在这里，我们讨论了药物发现如何受益于进化生物学的原理，并报告了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特征和应用来实现这一点，例如基于机器学习的多标准虚拟筛选。
