Sridharan Bhuvanesh, Mehta Sarvesh, Pathak Yashaswi, Priyakumar U Deva
Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
J Phys Chem Lett. 2022 Jun 9;13(22):4924-4933. doi: 10.1021/acs.jpclett.2c00624. Epub 2022 May 29.
Spectroscopy is the study of how matter interacts with electromagnetic radiation. The spectra of any molecule are highly information-rich, yet the inverse relation of spectra to the corresponding molecular structure is still an unsolved problem. Nuclear magnetic resonance (NMR) spectroscopy is one such critical technique in the scientists' toolkit to characterize molecules. In this work, a novel machine learning framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure given an NMR spectra. The proposed framework uses a combination of online Monte Carlo tree search (MCTS) and a set of graph convolution networks to build a molecule iteratively. Our method can predict the structure of the molecule ∼80% of the time in its top 3 guesses for molecules with <10 heavy atoms. We believe that the proposed framework is a significant step in solving the inverse design problem of NMR spectra.
光谱学是研究物质如何与电磁辐射相互作用的学科。任何分子的光谱都包含丰富的信息，然而光谱与相应分子结构之间的反向关系仍然是一个未解决的问题。核磁共振（NMR）光谱学是科学家用于表征分子的关键技术之一。在这项工作中，提出了一种新颖的机器学习框架，该框架试图通过在化学空间中导航来解决这个反向问题，即在给定核磁共振光谱的情况下找到正确的结构。所提出的框架结合了在线蒙特卡罗树搜索（MCTS）和一组图卷积网络来迭代构建分子。对于重原子数小于10的分子，我们的方法在其前3次猜测中约80%的时间能够预测出分子结构。我们相信，所提出的框架是解决核磁共振光谱反向设计问题的重要一步。
