ABSTRACT
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 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. Then, 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 insufficiency in the experimental evaluation standard which affects the fair evaluation of previous work. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-021-0970-3.
ABSTRACT
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 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. Then, 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 insufficiency in the experimental evaluation standard which affects the fair evaluation of previous work. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0970-3.
ABSTRACT
OBJECTIVES: The 2019 novel coronavirus (COVID-19) pandemic is a severe global crisis which has resulted in many public health problems. This study aimed to investigate the prevalence of poor sleep quality and its related factors among employees who returned to work during the COVID-19 pandemic. METHODS: Our online cross-sectional study included 2,410 participants aged ≥17 years in Deqing and Taizhou, Zhejiang Province, China from 5th to 14th March 2020. The questionnaire covered information on demographic characteristics, health status, workplace, lifestyle, attitude towards COVID-19, assessment of anxiety, depression and sleep quality. The Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) was administered to measure the poor sleep quality. Poor sleep quality was defined as a global PSQI score>5. Factors associated with sleep quality were analyzed by logistic regression models. RESULTS: In sum near half (49.2%) of 2,410 returning workers were females and the average year of subjects was 36.3 ± 9.1 years. The overall prevalence of poor sleep quality was 14.9% (95%CI: 13.5%-16.3%). The average score of PSQI was 3.0 ± 2.5 and average sleep duration was 7.6 ± 1.2 h. Independent related factors of poor sleep quality included age older than 24 years, higher education level, negative attitude towards COVID-19 control measures, anxiety and depression. CONCLUSIONS: Poor sleep quality was common and there was a shorter sleep duration among returning workers during the COVID-19 pandemic. Possible risk factors identified from this study may be of great importance in developing proper intervention for the targeted population to improve the sleep health during the COVID-19 public health emergency.