Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstr. 43, 20146 Hamburg, Germany.
insitro, 279 E Grand Ave., CA 94608, South San Francisco, USA.
Curr Opin Struct Biol. 2023 Jun;80:102578. doi: 10.1016/j.sbi.2023.102578. Epub 2023 Apr 4.
The size of actionable chemical spaces is surging, owing to a variety of novel techniques, both computational and experimental. As a consequence, novel molecular matter is now at our fingertips that cannot and should not be neglected in early-phase drug discovery. Huge, combinatorial, make-on-demand chemical spaces with high probability of synthetic success rise exponentially in content, generative machine learning models go hand in hand with synthesis prediction, and DNA-encoded libraries offer new ways of hit structure discovery. These technologies enable to search for new chemical matter in a much broader and deeper manner with less effort and fewer financial resources. These transformational developments require new cheminformatics approaches to make huge chemical spaces searchable and analyzable with low resources, and with as little energy consumption as possible. Substantial progress has been made in the past years with respect to computation as well as organic synthesis. First examples of bioactive compounds resulting from the successful use of these novel technologies demonstrate their power to contribute to tomorrow's drug discovery programs. This article gives a compact overview of the state-of-the-art.
可操作的化学空间的规模正在迅速扩大，这要归功于各种新颖的计算和实验技术。因此，现在新型分子物质就在我们的指尖，在药物发现的早期阶段不能也不应该忽视它们。具有高合成成功率的巨大、组合式、按需制造的化学空间的内容呈指数级增长，生成式机器学习模型与合成预测齐头并进，而 DNA 编码库则提供了发现新的命中结构的新方法。这些技术使我们能够以更少的努力和资源更广泛和深入地寻找新的化学物质。这些变革性的发展需要新的化学信息学方法来搜索和分析巨大的化学空间，同时尽可能地减少资源消耗和能量消耗。在计算和有机合成方面，过去几年已经取得了实质性的进展。成功使用这些新技术产生的生物活性化合物的第一个例子证明了它们为明天的药物发现计划做出贡献的能力。本文概述了最新的技术进展。
