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Optimizing blood-brain barrier permeability in KRAS inhibitors:A structure-constrained molecular generation approach 认领 引用 被引量：1
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作者 Xia Sheng Yike Gui +9 位作者 Jie Yu Yitian Wang Zhenghao Li Xiaoya Zhang Yuxin Xing Yuqing Wang Zhaojun Li Mingyue Zheng Liquan Yang Xutong Li 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第8期1848-1859,共12页
Kirsten rat sarcoma viral oncogene homolog(KRAS)protein inhibitors are a promising class of therapeutics,but research on molecules that effectively penetrate the blood-brain barrier(BBB)remains limited,which is crucia... Kirsten rat sarcoma viral oncogene homolog(KRAS)protein inhibitors are a promising class of therapeutics,but research on molecules that effectively penetrate the blood-brain barrier(BBB)remains limited,which is crucial for treating central nervous system(CNS)malignancies.Although molecular generation models have recently advanced drug discovery,they often overlook the complexity of biological and chemical factors,leaving room for improvement.In this study,we present a structureconstrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties.Our approach utilizes a variational autoencoder(VAE)generative model integrated with reinforcement learning for multi-objective optimization.This method specifically aims to enhance BBB permeability(BBBp)while maintaining high-affinity substructures of KRAS inhibitors.To support this,we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models.Additionally,we introduce two novel metrics,the knowledge-integrated reproduction score(KIRS)and the composite diversity score(CDS),to assess structural performance and biological relevance.Retrospective validation with KRAS inhibitors,AMG510 and MRTX849,demonstrates the framework’s effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications.This study provides a robust framework for accelerating the structural enhancement of lead compounds,advancing the drug development process across diverse targets. 展开更多
关键词 KRAS inhibitors Drug design Blood-brain barrier permeability Molecular optimization Deep learning Generation models
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Targeted creation of new mutants with compact plant architecture using CRISPR/Cas9 genome editing by an optimized genetic transformation procedure in cucurbit plants 认领 引用 被引量：26
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作者 Tongxu Xin Haojie Tian +10 位作者 Yalin Ma Shenhao Wang Li Yang Xutong Li Mengzhuo Zhang Chen Chen Huaisong Wang Haizhen Li Jieting Xu Sanwen Huang Xueyong Yang 《Horticulture Research》 SCIE CSCD 2022年第1期1224-1237,共14页
Fruits and vegetables in the Cucurbitaceae family,such as cucumber,melon,watermelon,and squash,contribute greatly to the human diet.The widespread use of genome editing technologies has greatly accelerated gene functi... Fruits and vegetables in the Cucurbitaceae family,such as cucumber,melon,watermelon,and squash,contribute greatly to the human diet.The widespread use of genome editing technologies has greatly accelerated gene functional characterization and crop improvement.However,most economically important cucurbit plants,including melon and squash,remain recalcitrant to standard Agrobacterium tumefaciens-mediated transformation,limiting the effective use of genome editing technology.In this study,we used an“optimal infiltration intensity”strategy to establish an efficient genetic transformation system for melon and squash.We harnessed the power of this method to target homologs of the ERECTA family of receptor kinase genes and created alleles that resulted in a compact plant architecture with shorter internodes in melon,squash,and cucumber.The optimized transformation method presented here enables stable CRISPR/Cas9-mediated mutagenesis and provides a solid foundation for functional gene manipulation in cucurbit crops. 展开更多
关键词 genome editing technologyin genome editing fruits vegetables genome editing technologies CRISPR Cas melon cucurbit plants gene functional characterization
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Effects of Rat Cytomegalovirus on the Nervous System of the Early Rat Embryo 认领 引用 被引量：5
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作者 Xiuning Sun YingJun Guan +6 位作者 Fengjie Li Xutong Li Xiaowen Wang Zhiyu Guan Kai Sheng Li Yu Zhijun Liu 《Virologica Sinica》 CAS CSCD 2012年第4期234-240,共7页
The purpose of the study was to investigate the impact of rat cytomegalovirus （RCMV） infection on the development of the nervous system in rat embryos, and to evaluate the involvement of Wnt signaling pathway key mo... The purpose of the study was to investigate the impact of rat cytomegalovirus （RCMV） infection on the development of the nervous system in rat embryos, and to evaluate the involvement of Wnt signaling pathway key molecules and the downstream gene neurogenin 1 （Ngnl） in RCMV infected neural stem cells （NSCs）. Infection and control groups were established, each containing 20 pregnant Wistar rats. Rats in the infection group were inoculated with RCMV by intraperitoneal injection on the first day of pregnancy. Rat E20 embryos were taken to evaluate the teratogenic rate. NSCs were isolated from El3 embryos, and maintained in vitro. We found： 1） Poor fetal development was found in the infection group with low survival and high malformation rates. 2） The proliferation and differentiation of NSCs were affected. In the infection group, NSCs proliferated more slowly and had a lower neurosphere formation rate than the control. The differentiation ratio from NSCs to neurons and glial cells was significantly different from that of the control, showed by immunofluorescenee staining. 3） Ngnl mRNA expression and the nuclear p-catenin protein level were significantly lower than the control on day 2 when NSCs differentiated. 4） The Morris water maze test was performed on 4-week pups, and the infected rats were found worse in learning and memory ability. In a summary, RCMV infection caused abnormalities in the rat embryonic nervous system, significantly inhibited NSC proliferation and differentiation, and inhibited the expression of key molecules in the Wnt/β-catenin signaling pathway so as to affect NSCs differentiation. This may be an important mechanism by which RCMV causes embryonic nervous system abnormalities. 展开更多
关键词 RCMV NSCs Proliferation and differentiation Wnt/β-catenin Ngnl
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Artificial intelligence in drug design 认领 引用 被引量：29
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作者 Feisheng Zhong Jing Xing +13 位作者 Xutong Li Xiaohong Liu Zunyun Fu Zhaoping Xiong Dong Lu Xiaolong Wu Jihui Zhao Xiaoqin Tan Fei Li Xiaomin Luo Zhaojun Li Kaixian Chen Mingyue Zheng Hualiang Jiang 《Science China(Life Sciences)》 SCIE CAS CSCD 2018年第10期1191-1204,共14页
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage... Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design. 展开更多
关键词 drug design artificial intelligence deep learning QSAR ADME/T
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In silico off-target profiling for enhanced drug safety assessment 认领 引用 被引量：4
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作者 Jin Liu Yike Gui +10 位作者 Jingxin Rao Jingjing Sun Gang Wang Qun Ren Ning Qu Buying Niu Zhiyi Chene Xia Sheng Yitian Wang Mingyue Zheng Xutong Li 《Acta Pharmaceutica Sinica B》 SCIE CAS CSCD 2024年第7期2927-2941,共15页
Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases.However,the economic burden associated with detecting drug off-targets and potential side effects t... Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases.However,the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial.Drug off-target interactions,along with the adverse drug reactions they induce,are significant factors affecting drug safety.To assess the liability of candidate drugs,we developed an artificial intelligence model for the precise prediction of compound off-target interactions,leveraging multi-task graph neural networks.The outcomes of off-target predictions can serve as representations for compounds,enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity.Furthermore,the predicted off-target profiles are employed in adverse drug reaction(ADR)enrichment analysis,facilitating the inference of potential ADRs for a drug.Using the withdrawn drug Pergolide as an example,we elucidate the mechanisms underlying ADRs at the target level,contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions.Overall,our work facilitates the early assessment of compound safetyoxicity based on off-target identification,deduces potential ADRs of drugs,and ultimately promotes the secure development of drugs. 展开更多
关键词 Drug safety Off-target prediction Adverse drug reactions Toxicity Molecular representation Artificial intelligence
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Omics-based large language models:A new engine for drug discovery innovation 认领 引用
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作者 Xia Sheng Xiaoya Zhang +5 位作者 Yuxin Xing Yuqi Shi Chuanlong Zeng Xiaochu Tong Mingyue Zheng Xutong Li 《Acta Pharmaceutica Sinica B》 SCIE CAS CSCD 2026年第1期122-136,共15页
Traditional drug discovery suffers from low efficiency and high attrition rates,largely due to the complexity and heterogeneity of human diseases.Omics technologies offer a systems-level perspective for uncovering dis... Traditional drug discovery suffers from low efficiency and high attrition rates,largely due to the complexity and heterogeneity of human diseases.Omics technologies offer a systems-level perspective for uncovering disease mechanisms and identifying therapeutic targets,but present challenges such as high dimensionality,noise,and heterogeneity.Large language models(LLMs),originally developed for natural language processing,are emerging as powerful tools to address these issues by capturing complex patterns and inferring missing information from large,noisy datasets.We present a three-part framework:(1)Analyzing how LLM architectures and learning paradigms handle challenges specific to genomics,transcriptomics,and proteomics data;(2)Detailing LLM applications in key areas:uncovering disease mechanisms,identifying drug targets,predicting drug response,and simulating cellular behavior;(3)Discussing how insights from omics-integrated LLMs can inform the development of drugs targeting specific pathways,moving beyond single targets towards strategies grounded in underlying disease biology.This framework provides both conceptual insights and practical guidance for leveraging LLMs in omics-driven drug discovery and development. 展开更多
关键词 Large language model Representation learning Generalization Omics integration Single-cell Perturbation modeling Target identification Drug discovery
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Drug target inference by mining transcriptional data using a novel graph convolutional network framework 认领 引用 被引量：10
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作者 Feisheng Zhong Xiaolong Wu +13 位作者 Ruirui Yang Xutong Li Dingyan Wang Zunyun Fu Xiaohong Liu XiaoZhe Wan Tianbiao Yang Zisheng Fan Yinghui Zhang Xiaomin Luo Kaixian Chen Sulin Zhang Hualiang Jiang Mingyue Zheng 《Protein & Cell》 SCIE CSCD 2022年第4期281-301,共21页
A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects.Recently,the fast accumulation of gen... A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects.Recently,the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology.Here,we propose a novel Siamese spectral-based graph convolutional network(SSGCN)model for inferring the protein targets of chemical compounds from gene transcriptional profiles.Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets,and the biological networks under different experiment conditions further complicate the situation,the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles.On a benchmark set and a large time-split validation dataset,the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map.Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound,or reversely,in finding novel inhibitors of a given target of interest. 展开更多
关键词 drug target inference transcriptomics deep learning experimental verification
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Changing the strategy and culture of stroke awareness education in China: implementing Stroke 1-2-0 认领 引用 被引量：14
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作者 Jing Zhao Xutong Li +5 位作者 Xiaochuan Liu Yuming Xu Jihong Xu Anding Xu Yongjun Wang Renyu Liu 《Stroke & Vascular Neurology》 SCIE 2020年第4期374-380,共7页
This project implemented the Stroke1-2-0 stroke awareness programme across China and investigated its impact over a 2-year period.We initiated the Stroke1-2-0 educational campaign and Stroke1-2-0 special task forces(S... This project implemented the Stroke1-2-0 stroke awareness programme across China and investigated its impact over a 2-year period.We initiated the Stroke1-2-0 educational campaign and Stroke1-2-0 special task forces(STF)across the nation.Massive media coverage,community-based educational sessions with videos and other related materials and induction of Stroke1-2-0 STF were the major means of promotion.We delivered a survey at the end of 2016 and 2018 to evaluate the impact of our effort.A total of 3066 participants responded to the first survey in 2016,and 15207 participants responded in 2018 across China.The acceptance rate for Stroke1-2-0 versus FAST(an English-language stroke awareness tool)was 50.2%versus 19.1%in 2016,and changed significantly to 82.2%versus 8.0%in 2018(p<0.001).Stroke1-2-0 was well accepted by all ages and by people with different academic qualifications.Only 6.5%of survey respondents were aware that there was a therapeutic window for thrombolytic therapy in 2016,but this awareness increased significantly to 32.8%in 2018.Only 12.6%of people in 2016 indicated that they would send patients with stroke to the nearest hospital capable of performing thrombolytic therapy,but there was a nearly threefold increase(52.5%)in this number by 2018.More than 1000 major hospitals joined the Stroke1-2-0 STF,and more than 20000‘stroke warriors’have joined our stroke awareness improvement effort so far.Stroke1-2-0 stroke awareness programme is well-implemented and accepted,and is generating profound improvement in stroke awareness in China. 展开更多
关键词 education culture awareness
An overview of recent advances and challenges in predicting compound-protein interaction(CPI) 认领 引用 被引量：2
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作者 Yanbei Li Zhehuan Fan +4 位作者 Jingxin Rao Zhiyi Chen Qinyu Chu Mingyue Zheng Xutong Li 《Medical Review》 2023年第6期465-486,共22页
Compound-protein interactions(CPIs)are critical in drug discovery for identifying therapeutic targets,drug side effects,and repurposing existing drugs.Machine learning(ML)algorithms have emerged as powerful tools for ... Compound-protein interactions(CPIs)are critical in drug discovery for identifying therapeutic targets,drug side effects,and repurposing existing drugs.Machine learning(ML)algorithms have emerged as powerful tools for CPI prediction,offering notable advantages in cost-effectiveness and efficiency.This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models,highlighting their performance and achievements.It also offers insights into CPI prediction-related datasets and evaluation benchmarks.Lastly,the article presents a comprehensive assessment of the current landscape of CPI prediction,elucidating the challenges faced and outlining emerging trends to advance the field. 展开更多
关键词 compound-protein interaction prediction drug discovery artificial intelligence scoring function chemogenomics
人工智能算法在药物细胞敏感性预测中的应用 认领 引用 被引量：3
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作者 李叙潼 吴小龙 +7 位作者 万晓喆 钟飞盛 崔晨 陈颖佳 陈立凡 陈凯先 蒋华良 郑明月 《科学通报》 EI CAS CSCD 北大核心 2020年第32期3551-3561,共11页
开发基于癌症患者基因组信息预测有效治疗策略的计算模型是精准医学中的关键挑战.近年来,国际多个组织机构公开了针对数百种细胞系的多层次的基因组表征数据.将这类组学数据与体外肿瘤细胞系的药物细胞敏感性相结合,研究人员可以剖析癌... 开发基于癌症患者基因组信息预测有效治疗策略的计算模型是精准医学中的关键挑战.近年来,国际多个组织机构公开了针对数百种细胞系的多层次的基因组表征数据.将这类组学数据与体外肿瘤细胞系的药物细胞敏感性相结合,研究人员可以剖析癌症治疗药物的分子机制,并将其转化为精准医学所需的个性化诊疗策略.基于大数据的人工智能算法在基因组学与药物响应之间建立了新的桥梁,推进了肿瘤细胞中药物敏感性的预测算法的发展.本文首先对公开的基因组表征数据集进行了总结,随后介绍了基因组表征数据和包括机器学习算法、网络算法和多模态神经网络算法在内的人工智能算法在癌细胞的药物敏感性预测中的应用案例.基于网络的预测方法和多模态深度学习方法有利于实现多组学数据的系统性的整合和应用,能克服传统的机器学习方法在药物响应预测中的局限性,是今后药物敏感性研究的发展方向. 展开更多
关键词 药物敏感性 机器学习 网络 多模态
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