Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
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DOI：
10.48550/arxiv.2204.01847
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Qi Zhang-;Chang Liu;Stephen Wu;Ryo Yoshida
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文献类型：
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作者：
Qi Zhang-;Chang Liu;Stephen Wu;Ryo Yoshida
在过去的几年里，使用机器学习的从头分子设计取得了巨大的技术进步，但其实际部署并不成功。这主要是由于合成这种计算设计的分子的成本和技术难度。为了克服这些障碍，近年来已经深入研究了使用深度神经网络进行合成路线设计的各种方法。然而，在同时设计分子和合成路线方面进展甚微。在这里，我们制定的问题，同时设计分子所需的一组属性和合成路线的框架内贝叶斯推理。设计变量由反应网络中的一组反应物及其网络拓扑结构组成。设计空间是非常大的，因为它由购买反应物的所有组合组成，通常在数百万或更多的数量级。此外，所设计的反应网络可以采用简单多步线性反应路线之外的任何拓扑结构。为了解决这个困难的组合问题，我们提出了一个功能强大的顺序蒙特卡罗算法，递归地设计一个合成反应网络，依次建立单步反应。在基于市售化合物设计类药物分子的案例研究中，与启发式组合搜索方法相比，所提出的方法在计算效率和覆盖率以及现有化合物的新奇性方面表现出压倒性的性能。
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owing to the cost and technical difficulty of synthesizing such computationally designed molecules. To overcome such barriers, various methods for synthetic route design using deep neural networks have been studied intensively in recent years. However, little progress has been made in designing molecules and their synthetic routes simultaneously. Here, we formulate the problem of simultaneously designing molecules with the desired set of properties and their synthetic routes within the framework of Bayesian inference. The design variables consist of a set of reactants in a reaction network and its network topology. The design space is extremely large because it consists of all combinations of purchasable reactants, often in the order of millions or more. In addition, the designed reaction networks can adopt any topology beyond simple multistep linear reaction routes. To solve this hard combinatorial problem, we present a powerful sequential Monte Carlo algorithm that recursively designs a synthetic reaction network by sequentially building up single-step reactions. In a case study of designing drug-like molecules based on commercially available compounds, compared with heuristic combinatorial search methods, the proposed method shows overwhelming performance in terms of computational efficiency and coverage and novelty with respect to existing compounds.