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A2Str: molecular Graph generation Based on Given Atoms

国际计算机前沿大会会议论文集 2021年第1期 417-428页

作者： Xilong Wang Heilongjiang University Harbin 150080HeilongjiangChina

molecular graph generation is an emerging area of research withnumerous applications. Most existing approaches generate new molecules bylearning a generative model from a collection of known molecules. However,how to obtain the molecular structure from its atomic composition is a problemto be solved. In this paper, it proposes an autoencoder-based model called A2Strwhich takes the atomic composition of a molecular as input. Its molecular structurewas generated and model structures were designed. The effects were verified oneby one. In addition, experiments were implemented with isomer generation anddetermined that the ratio of more than 3% of molecules with different structurescan be obtained from the same combination of atoms.

关键词： molecular generation Atomic composition Autoencoder

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Towards Exploring Large molecular Space:An Efficient Chemical Genetic Algorithm

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Journal of Computer Science & Technology 2022年第6期37卷 1464-1477页

作者：朱健甫郝中楷刘淇阴钰陆承镪黄振亚陈恩红 Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Generating molecules with desired properties is an important task in chemistry and pharmacy.An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19.Data mining and artificial intelligence may be good ways to find an efficient method.Recently,both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s properties.However,existing methods have defects in the experimental evaluation standards.These methods also need to be improved in efficiency and performance.To solve these problems,we propose a method named the Chemical Genetic Algorithm for Large molecular Space(CALM).Specifically,CALM employs a scalable and efficient molecular representation called molecular matrix.And we design corresponding crossover,mutation,and mask operators inspired by domain knowledge and previous studies.We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization.The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods,where the z tests performed on the results of several experiments show that our method is more than 99%likely to be significant.At the same time,based on the experimental results,we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous work.Avoiding these defects helps to objectively evaluate the performance of all work.

关键词： data mining molecular generation genetic algorithm drug discovery artificial intelligence

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