【学术视频】机器学习-计算化学Workshop | 北京大学林康杰博士

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图 | 林康杰

题   目：Automatic Retrosynthetic Route Planning Using Template-Free Models

报告人：林康杰

单   位：北京大学

时   间：2019-09-05

地   点：厦门大学化学化工学院

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报告摘要

Retrosynthetic route planning can be considered a rule-based reasoning procedure. The possibilities for each transformation are generated based on collected reaction rules, and then potential reaction routes are recommended by various optimization algorithms. Although there has been much progress in computer-assisted retrosynthetic route planning and reaction prediction, fully data-driven automatic retrosynthetic route planning remains challenging. Here we present a template-free approach that is independent of any reaction template, rules, or atom mapping, to implement automatic retrosynthetic route planning. We treated each reaction prediction task as a data-driven sequence-to-sequence problem using the multi-head attention-based Transformer architecture, which has demonstrated power in machine translation tasks. Using reactions from United States patent literature, our end-to-end models naturally incorporate the global chemical environments of molecules and achieve state-of-the-art performance on top-1 predictive accuracy (63.0%) and top-1 molecular validity (99.6%) in one-step retrosynthetic tasks. Inspired by the success rate of the one-step reaction prediction, we further carried out iterative, multi-step retrosynthetic route planning for four case products, which was successful. We then constructed an automatic data-driven end-to-end retrosynthetic route planning system (AutoSynRoute) using Monte Carlo Tree Search with a heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The end-to-end model for reaction task prediction can be easily extended to larger or customerrequested reaction databases. Our study presents an important step in realizing automatic retrosynthetic route planning.

个人简介

Kangjie Lin is pursuing his PhD degree in computational chemistry in Peking University (2017~) under the supervision of Professor Luhua Lai. He is currently engaged in applying machine learning techniques to computer-aided synthesis planning and molecular property prediction. He is also interested in de novo molecule design and automated synthesis of chemicals.

会议简介

2019年9月3日-6日，由固体表面物理化学国家重点实验室（厦门大学）、福建省理论与计算化学重点实验室和厦门大学化学化工学院主办的“机器学习-计算化学Workshop”在厦门大学化学化工学院举办。本次Workshop邀请了相关领域的研究者报告领域前沿进展，并设置Hands-on tutorials环节帮助学员们熟悉代码的使用。此次Workshop的举办增进了不同领域研究者的交流，促进了开源共享的观念传递，希望推动大数据技术在计算化学和材料模拟等领域的应用。

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