Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
Methods Mol Biol. 2020;2114:1-17. doi: 10.1007/978-1-0716-0282-9_1.
Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.
药物研发是一项昂贵、耗时且高风险的工作。为了避免后期失败，从过去项目中吸取经验教训和开发新方法至关重要。新的治疗方法和新兴的新靶标空间允许探索前所未有的适应症或解决迄今无法成药的靶标。后期淘汰通常归因于缺乏疗效或与化合物相关的安全问题。已证明疗效与人类疾病的强烈遗传联系、对靶标生物学的更好理解以及生物标志物从动物到人类的转化有关。通过配体优化可以提高化合物的安全性，而对于难靶标，配体优化的要求越来越高。因此，新策略包括设计别构配体、共价结合物和其他治疗方法。目前，设计方法严重依赖人工智能和先进的计算方法，如自由能计算和量子化学。特别是对于量子化学方法，本章提供了更详细的概述。
