Biotherapeutics and Medicinal Sciences, Biogen, Cambridge, MA, USA.
Methods Mol Biol. 2022;2390:409-419. doi: 10.1007/978-1-0716-1787-8_17.
Development of computer-aided de novo design methods to discover novel compounds in a speedy manner to treat human diseases has been of interest to drug discovery scientists for the past three decades. In the beginning, the efforts were mostly concentrated to generate molecules that fit the active site of the target protein by sequential building of a molecule atom-by-atom and/or group-by-group while exploring all possible conformations to optimize binding interactions with the target protein. In recent years, deep learning approaches are applied to generate molecules that are iteratively optimized against a binding hypothesis (to optimize potency) and predictive models of drug-likeness (to optimize properties). Synthesizability of molecules generated by these de novo methods remains a challenge. This review will focus on the recent development of synthetic planning methods that are suitable for enhancing synthesizability of molecules designed by de novo methods.
在过去的三十年里，开发计算机辅助从头设计方法以快速发现新化合物来治疗人类疾病一直是药物发现科学家感兴趣的领域。起初，人们主要致力于通过逐个原子和/或逐个基团地构建分子，同时探索所有可能的构象来生成适合靶蛋白活性部位的分子，从而产生与靶蛋白结合的优化相互作用。近年来，深度学习方法被应用于生成分子，这些分子根据结合假说（优化效力）和药物相似性的预测模型（优化性质）进行迭代优化。这些从头设计方法生成的分子的可合成性仍然是一个挑战。本文综述了最近开发的合成规划方法，这些方法适用于提高从头设计方法设计的分子的可合成性。
