Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios.

中文翻译：

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使用多模态变换器神经网络进行深度支架跳跃

支架跳跃是现代药物化学合理药物设计的核心任务，其目的是设计对已知命中分子具有相似目标生物​​活性的新型支架分子。传统上，脚手架跳跃依赖于搜索无法利用巨大化学空间的可用化合物的数据库。在这项研究中，我们将此任务重新制定为监督分子到分子的翻译，以生成在 2D 结构中新颖但在 3D 结构中相似的跳跃分子，这是受候选化合物通过 3D 构象与其目标结合这一事实的启发。为了有效地训练模型，我们从公共生物活性数据库中挑选了超过 5 万对生物活性增加、3D 结构相似但2D 结构不同的分子，其中涵盖了药物化学家通常研究的 40 种激酶。此外，我们通过空间图神经网络集成了分子 3D 构象异构体，并通过 Transformer 集成了蛋白质序列信息，从而设计了一种多模态分子转换器架构。经过训练的 DeepHop 模型显示能够生成大约 70% 的分子，这些分子具有更高的生物活性以及与模板分子的高 3D 相似性但低 2D 支架相似性。这一比率是其他最先进的深度学习方法以及基于规则和虚拟筛选的方法的 1.9 倍。此外，我们证明了该模型可以通过对一小组活性化合物进行微调来推广到新的目标蛋白质。案例研究还展示了 DeepHop 在实际脚手架跳跃场景中的优势和实用性。