一种寻找分子合成DAG的方法
Barking up the right tree: an approach to search over molecule synthesis DAGs
摘要：
当设计具有特定性质的新分子时，重要的不仅是要制造什么，更重要的是如何制造它。这些指令形成了一个合成有向无环图（DAG），描述了如何通过化学反应递归组合大量简单的构建块，以创建更复杂的目标分子。相比之下，目前许多分子的深度生成模型忽略了可合成性。因此，我们提出了一个深度生成模型，通过直接输出分子合成DAG来更好地代表现实世界的过程。我们认为，这提供了合理的归纳偏差，确保我们的模型搜索相同的化学空间，化学家也可以访问，以及可解释性。我们表明，我们的方法能够很好地模拟化学空间，产生各种不同的分子，并允许无约束的优化一个固有的约束问题：最大限度地提高某些化学性质，使发现的分子是可合成的。
英文摘要：
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interpretability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable.
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