Mihalovits Levente M, Szalai Tibor V, Bajusz Dávid, Keserű György M
Medicinal Chemistry Research Group and Drug Innovation Centre, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary.
Department of Inorganic and Analytical Chemistry, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
J Chem Inf Model. 2024 Dec 9;64(23):8963-8979. doi: 10.1021/acs.jcim.4c00803. Epub 2024 Sep 21.
The concept of DNA-encoded libraries (DELs) enables the experimental screening of billions of compounds simultaneously, offering an unprecedented boost in the coverage of chemical space. In parallel, however, dramatically increased access to supercomputers and a number of ultrahigh throughput virtual screening (uHTVS) tools have made screening of billion-membered virtual libraries available. Here, we investigate whether current, brute-force, or AI-enabled uHTVS approaches might constitute a computational alternative to DEL screening. While it is tempting to look at uHTVS as a computational analogue of DEL screening, we found specific advantages and limitations of both methodologies that suggest them being complementary rather than competitive.
DNA编码文库(DELs)的概念使得能够同时对数十亿种化合物进行实验筛选,极大地拓展了化学空间的覆盖范围。然而,与此同时,超级计算机的使用便利性大幅提升,并且出现了许多超高通量虚拟筛选(uHTVS)工具,从而使得对包含数十亿成员的虚拟文库进行筛选成为可能。在此,我们研究当前的、强力的或基于人工智能的uHTVS方法是否可能构成DEL筛选的一种计算替代方案。虽然将uHTVS视为DEL筛选的计算类似物很诱人,但我们发现这两种方法都有各自的优势和局限性,这表明它们是互补而非竞争的关系。