Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami School of Medicine, Miami, Florida, USA.
Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami School of Medicine, Miami, Florida, USA.
J Invest Dermatol. 2019 Dec;139(12):2400-2408.e1. doi: 10.1016/j.jid.2019.06.129.
Drug discovery is a complex process with many potential pitfalls. To go to market, a drug must undergo extensive preclinical optimization followed by clinical trials to establish its efficacy and minimize toxicity and adverse events. The process can take 10-15 years and command vast research and development resources costing over $1 billion. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. With the increasing availability of large public datasets (big data) and computational capabilities, data science is quickly becoming a key component of the drug discovery pipeline. One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. Molecular modeling involves the study of the chemical structure of a drug and how it interacts with a potential disease-relevant target, as well as predicting its ADMET properties. The scope of molecular modeling is wide and complex. Here we specifically discuss docking, a tool commonly employed for studying drug-target interactions. Docking allows for the systematic exploration of how a drug interacts at a protein binding site and allows for the rank-ordering of drug libraries for prioritization in subsequent studies. This process can be efficiently used to virtually screen libraries containing over millions of compounds.
药物发现是一个复杂的过程,存在许多潜在的陷阱。要推向市场,药物必须经过广泛的临床前优化,然后进行临床试验,以确定其疗效并最大程度地降低毒性和不良反应。这个过程可能需要 10-15 年的时间,并需要投入大量的研发资源,成本超过 10 亿美元。美国新药批准的成功率<15%,而且投资成本往往无法收回。随着大量公共数据集(大数据)和计算能力的日益普及,数据科学正在迅速成为药物发现管道的关键组成部分。其中一种计算方法,即大规模分子建模,在临床前命中和先导化合物鉴定过程中至关重要。分子建模涉及研究药物的化学结构以及它如何与潜在的疾病相关目标相互作用,以及预测其 ADMET 特性。分子建模的范围很广,也很复杂。在这里,我们特别讨论对接,这是一种常用于研究药物-靶标相互作用的工具。对接允许系统地探索药物在蛋白质结合位点的相互作用方式,并对药物库进行排序,以便在后续研究中进行优先级排序。这个过程可以有效地用于虚拟筛选包含数百万种化合物的库。