摘要/Abstract

新型有机分子一直是有机化学领域的研究重点, 其在开发高性能材料方面具有重要意义. 传统的有机分子发现是一个类似于“炒菜”的试错过程, 它耗时耗能且效率相对低下. 常见的量子化学方法试图根据期望属性值筛选出合理的分子结构, 以更好地指导实验, 然而, 由于计算资源相对于算法复杂度严重不足, 精确给出实验指导在大多数情况下难以实现. 近年来机器学习的出现改变了这种情况, 训练好的模型可以快速推测出分子的属性. 更令人兴奋的是机器学习可以逆向进行分子设计, 拓宽人类的想象力, 给出其在分子设计领域的“神之一手”. 本综述首先介绍了逆向分子设计所必须的分子描述方式, 随后对几种常见的深度生成模型加以归纳, 对新型有机分子设计研究现状进行了总结, 最后探讨了新型有机分子设计所面临的挑战, 展示了笔者做出的部分探索.
关键词: 机器学习, 生成模型, 逆向分子设计, 分子描述, BASE64编码
Low-cost and high-performance materials have become more and more important in past decades. It exhibits the technology level of a country. Chemists used to find the candidate material according to property regression and quantitative structure activity relationship (QSAR). Traditional methods focus on finding new molecule from prior knowledge with trial and error experiments. They are time-consuming and low efficiency on screening molecules. The appearance of machine learning (ML) changes this embarrassing situation in two ways. One is accelerating the property prediction process to prevent wasting time on worse candidates. The other is inverse molecule design which expands the imagination of human. Lots of researches show promising results using different inverse design method such as, variational auto-encoder (VAE), generative adversarial networks (GAN), reinforcement learning (RL), and recurrent neural network (RNN). They introduce uncertainty from different level to generate new structure candidates. In any method, molecule descriptor has a great impact on the result. The descriptor converts the 3D structures in real world to a vector or a notation string to feed into all kinds of ML models. Large number of descriptors have been developed in cheminformatic, bioinformatic, quantum chemistry and natural language process (NLP). Some classical descriptors are Coulomb matrix (CM), smooth overlap of atomic positions (SOAP), weighted graph (WG), simplified molecular input line entry specification (SMILES). They show different advantages and solving problems from different aspects. CM has clear definition and good result on energy regression. SOAP is good at reflecting local environment features of an atom. However, they are easy to encode but hard to decode. That is a reason why people prefer WG and SMILES in the structure inverse design tasks. WG and SMILES express structure as a graph (an atom as a node and a bond as an edge) or string to apply massive mature GNN or NLP algorithm on them. Nowadays, most of the ML applications on chemistry and molecule science are focus on developing new model to regress properties. However, it is thought that there is still large improving space on inverse design methods and traditional descriptors. In this paper, WG and SMILES are briefly introduced firstly. Then, four generative models are presented, including VAE, GAN, RL and RNN. Further, the current progress and challenges of inverse design methods are summarized case by case. Finally, some of the author՚s understanding and explorations are given out. It is proved that SMILES with BASE64 preprocessed shows some advantages on molecular reconstruction and worth to study deeply in future.
Key words: machine learning, generative model, inverse molecule design, molecule description, BASE64 encoding


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