Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary.
Expert Opin Drug Discov. 2022 Jun;17(6):629-640. doi: 10.1080/17460441.2022.2085685. Epub 2022 Jun 14.
Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches.
After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies.
Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.
实验筛选和虚拟筛选为超过 50%的临床候选药物的发现做出了贡献。考虑到这两个概念和目标相似,早期药物发现将受益于这些方法的有效整合。
在回顾了实验筛选和虚拟筛选的最新趋势后,作者讨论了从平行、集中、顺序和迭代筛选等不同的整合策略。在一些实际案例研究中展示了战略考虑因素。
实验筛选和虚拟筛选是互补的方法,应在发现先导化合物的过程中进行整合。虚拟筛选可以访问极其庞大的、具有合成可行性的化学空间,这些空间可以在 GPU 集群或云架构上进行有效搜索。高通量筛选(HTS)应用提供了可靠的数据集,而 DNA 编码文库(DEL)扩大了这些技术所涵盖的化学空间。这些发展,以及人工智能方法的使用,为它们的有效整合提供了新的选择。这里讨论的案例研究展示了互补策略(如集中和迭代筛选)的优势。