Background: Alzheimer's disease(AD) is a complex neurodegenerative disease. Due to the complexity of its molecular pathogenesis and the interaction of the numerous factors involved, the etiology and pathogenesis o...Background: Alzheimer's disease(AD) is a complex neurodegenerative disease. Due to the complexity of its molecular pathogenesis and the interaction of the numerous factors involved, the etiology and pathogenesis of AD have not been fully elucidated. Therefore, effective treatment for AD remains to be developed. Evodiamine, a quinolone alkaloid, has been found to improve learning and memory ability to in the APP^(swe)/PS1^(ΔE9) mouse model of dementia. However, the cytotoxicity and physicochemical properties of evodiamine have limited its use in the treatment of AD.Methods: Evodiamine and its derivatives were effectively synthesized by EDCImediated condensation at room temperature. These target compounds contained 1 thio-and 21 oxo-evodiamine derivatives with different substituted groups. The cytotoxicity of evodiamine and its derivatives and the neuroprotective effects of the evodiamine derivatives against H_2O_2-induced cell loss in SH-SY5 Y cells were investigated using the WST-8 assay. The Morris water-maze test was used to detect the effect of evodiamine and its derivatives on improving learning and memory in APP^(swe)/PS1^(ΔE9) mice.Results: In this study, a series of oxo-and thio-evodiamine derivatives was synthesized. Several derivatives showed lower cytotoxicity and stronger neuroprotective effects than evodiamine and elicited enhanced cognitive improvement, especially in the test of spatial memory in APP^(swe)/PS1^(ΔE9) mice.Conclusion: Our study provides insights for developing novel evodiamine derivatives for chemical intervention and treatment of AD.展开更多
Chemical reactions,which transform one set of substances to another,drive research in chemistry and biology.Recently,computer-aided chemical reaction prediction has spurred rapidly growing interest,and various deep le...Chemical reactions,which transform one set of substances to another,drive research in chemistry and biology.Recently,computer-aided chemical reaction prediction has spurred rapidly growing interest,and various deep learning-based algorithms have been proposed.However,current efforts primarily focus on developing models that support specific applications,with less emphasis on building unified frameworks that predict chemical reactions.Here,we developed Bidirectional Chemical Intelligent Net(Bi CINet),a prediction framework based on Bidirectional and Auto-Regressive Transformers(BARTs),for predicting chemical reactions in various tasks,including the bidirectional prediction of organic synthesis and enzyme-mediated chemical reactions.This versatile framework was trained using general chemical reactions and achieved top-1 forward and backward accuracies of 80.7%and 48.6%,respectively,for the public benchmark dataset USPTO_50K.By multitask transfer learning and integrating various task prompts into the model,Bi CINet enables retrosynthetic planning and metabolic prediction for small molecules,as well as retrosynthetic analysis and enzyme-catalyzed product prediction for natural products.These results demonstrate the superiority of our multifunctional framework for comprehensively understanding chemical reactions.展开更多
基金National Natural Science Foundation of China,Grant/Award Number 31970508Drug Innovation Major Project,Grant/Award Number 2018ZX09711-001-005Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences,Grant/Award Number CAMS-I2M and 2016-I2M-1-004。
文摘Background: Alzheimer's disease(AD) is a complex neurodegenerative disease. Due to the complexity of its molecular pathogenesis and the interaction of the numerous factors involved, the etiology and pathogenesis of AD have not been fully elucidated. Therefore, effective treatment for AD remains to be developed. Evodiamine, a quinolone alkaloid, has been found to improve learning and memory ability to in the APP^(swe)/PS1^(ΔE9) mouse model of dementia. However, the cytotoxicity and physicochemical properties of evodiamine have limited its use in the treatment of AD.Methods: Evodiamine and its derivatives were effectively synthesized by EDCImediated condensation at room temperature. These target compounds contained 1 thio-and 21 oxo-evodiamine derivatives with different substituted groups. The cytotoxicity of evodiamine and its derivatives and the neuroprotective effects of the evodiamine derivatives against H_2O_2-induced cell loss in SH-SY5 Y cells were investigated using the WST-8 assay. The Morris water-maze test was used to detect the effect of evodiamine and its derivatives on improving learning and memory in APP^(swe)/PS1^(ΔE9) mice.Results: In this study, a series of oxo-and thio-evodiamine derivatives was synthesized. Several derivatives showed lower cytotoxicity and stronger neuroprotective effects than evodiamine and elicited enhanced cognitive improvement, especially in the test of spatial memory in APP^(swe)/PS1^(ΔE9) mice.Conclusion: Our study provides insights for developing novel evodiamine derivatives for chemical intervention and treatment of AD.
基金financially supported by the National Natural Science Foundation of China(NSFC,No.82073692)CAMS Innovation Fund for Medical Sciences(CIFMS,No.2021-I2M-1-028)。
文摘Chemical reactions,which transform one set of substances to another,drive research in chemistry and biology.Recently,computer-aided chemical reaction prediction has spurred rapidly growing interest,and various deep learning-based algorithms have been proposed.However,current efforts primarily focus on developing models that support specific applications,with less emphasis on building unified frameworks that predict chemical reactions.Here,we developed Bidirectional Chemical Intelligent Net(Bi CINet),a prediction framework based on Bidirectional and Auto-Regressive Transformers(BARTs),for predicting chemical reactions in various tasks,including the bidirectional prediction of organic synthesis and enzyme-mediated chemical reactions.This versatile framework was trained using general chemical reactions and achieved top-1 forward and backward accuracies of 80.7%and 48.6%,respectively,for the public benchmark dataset USPTO_50K.By multitask transfer learning and integrating various task prompts into the model,Bi CINet enables retrosynthetic planning and metabolic prediction for small molecules,as well as retrosynthetic analysis and enzyme-catalyzed product prediction for natural products.These results demonstrate the superiority of our multifunctional framework for comprehensively understanding chemical reactions.
