Drug discovery is a complex and highly systematic process encompassing multiple critical stages,including target identification,bioactive molecule discovery,preclinical research,clinical trials,regulatory review,post-...Drug discovery is a complex and highly systematic process encompassing multiple critical stages,including target identification,bioactive molecule discovery,preclinical research,clinical trials,regulatory review,post-marketing surveillance,and others[1].This process typically spans many years and is often accompanied by high failure rates and substantial resource consumption.In recent years,driven by large amounts of biomedical data,artificial intelligence(AI)has begun to reshape every stage of drug discovery[2].Particularly,by integrating diverse,high-dimensional datasets with powerful predictive and generative models.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
Anemoside B4(AB4),a triterpenoidal saponin derived from Pulsatilla chinensis,has garnered considerable attention for its potent anti-inflammatory and immunomodulatory activities,culminating in its approval for clinica...Anemoside B4(AB4),a triterpenoidal saponin derived from Pulsatilla chinensis,has garnered considerable attention for its potent anti-inflammatory and immunomodulatory activities,culminating in its approval for clinical trials by the Center for Drug Evaluation,National Medical Products Administration,for the treatment of mild to moderate ulcerative colitis.Despite this,AB4’s therapeutic potential remained underexplored until the development of its injection formulation.This review discusses the scientific rationale and theoretical framework behind AB4’s development,offering a new paradigm and innovative research strategy for discovering lead compounds or drug candidates from natural medicines.In-depth investigations into AB4’s cellular targets,biochemical pathways,and administration routes have provided valuable insights into its druggability evaluation and clinical potential.The high water solubility of AB4,attributable to its multiple sugar units,imposes limitations on its bioavailability and pharmacokinetic profiles.To address this,structural modification via chemical methods and enzymatic hydrolysis have been employed,resulting in derivatives with reduced molecular weight,improved bioavailability,enhanced pharmacological activity,and greater clinical potential.These advances lay a solid foundation for the continued development of AB4 and its derivatives as promising therapeutic agents.展开更多
This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.展开更多
In the realm of drug discovery,recent advancements have paved the way for innovative approaches and methodologies.This comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light o...In the realm of drug discovery,recent advancements have paved the way for innovative approaches and methodologies.This comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light on novel strategies in drug development.(a)The resurgence of covalent drugs is highlighted,focusing on the targeted covalent inhibitors(TCIs)and their role in enhancing selectivity and affinity.(b)The potential of the quantum mechanics-based computational aid drug design(CADD)tool,Cov_DOX,is introduced for predicting protein-covalent ligand binding structures and affinities.(c)The scaffolding function of proteins is proposed as a new avenue for drug design,with a focus on modulating protein-protein interactions through small molecules and proteolysis targeting chimeras(PROTACs).(d)The concept of pro-PROTACs is explored as a promising strategy for cancer therapy,combining the principles of prodrugs and PROTACs to enhance specificity and reduce toxicity.(e)The design of prodrugs through carbon-carbon bond cleavage is discussed,offering a new perspective for the activation of drugs with limited modifiable functional groups.(f)The targeting of programmed cell death pathways in cancer therapies with small molecules is reviewed,emphasizing the induction of autophagy-dependent cell death,ferroptosis,and cuproptosis.These insights collectively contribute to a deeper understanding of the dynamic landscape of drug discovery.展开更多
Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to a...Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to accomplish industrial purposes.Systems biology seeks to understand biology at multiple dimensions,beginning with the molecular and cellular level and progressing to the tissues and organismal level and characterizes cells as complex information-processing systems.SynBio,on the other hand,toggles further and strives to develop and create its systems from scratch.SynBio is now applied in the development of novel therapeutic drugs for the prevention of human diseases,scale up industrial processes,and accomplish previously unfeasible industrial outcomes.This is made possible through significant breakthroughs in DNA sequencing and synthesis technology,as well as insights gained from synthetic chemistry and systems biology.SynBio technologies have allowed for the introduction of improved and synthetic metabolic functionalities in microorganisms to enable the synthesis of a range of pharmacologically-relevant compounds for pharmaceutical exploration.SynBio applications range from finding new ways to making industrial chemical synthesis processes more sustainable as well as the microbial synthesis of improved therapeutic modalities.Hence,this study underpins several innovations,auspicious potentials,and future directions afforded by SynBio that proposes improved industrial microbial synthesis for pharmaceutical exploration.展开更多
Endometrial cancer is the most common gynecologic cancer diagnosed in the United States and mortality is on the rise.Advanced and recurrent endometrial cancer represents a treatment challenge as historically there hav...Endometrial cancer is the most common gynecologic cancer diagnosed in the United States and mortality is on the rise.Advanced and recurrent endometrial cancer represents a treatment challenge as historically there have been limited therapeutic options for patients.In the last several years,multiple practice-changing clinical trials have led to significant improvements in the treatment landscape.This review will cover updates in the treatment and management of advanced and recurrent endometrial cancer with a focus on novel therapeutics,such as anti-PD-L1 and PD-1 inhibitors,poly ADP-ribose polymerase(PARP)inhibitors,antibody-drug conjugates,and hormonal therapy.展开更多
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to...The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.展开更多
Zeolites are crystalline microporous materials widely used in catalysis,adsorption,and ion exchange owing to their tunable pore structures and acid centers[1].Traditional zeolites,however,often suffer from limitations...Zeolites are crystalline microporous materials widely used in catalysis,adsorption,and ion exchange owing to their tunable pore structures and acid centers[1].Traditional zeolites,however,often suffer from limitations such as restricted molecular diffusion and rapid coking,which hinder their efficiency in processing large molecules.展开更多
Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distr...Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.展开更多
Pesticides play a pivotal role in modern agriculture. However, the pesticide industry faces significant challenges closely linked to major global concerns such as pesticide resistance, environmental pollution, food sa...Pesticides play a pivotal role in modern agriculture. However, the pesticide industry faces significant challenges closely linked to major global concerns such as pesticide resistance, environmental pollution, food safety, and crop yields. Developing safe, efficient, and environmentally friendly pesticides has become a key challenge for the industry. Recently, Qing Yang and colleagues unveiled the mode of action of a dual-functional protein, the ABCH transporter, which plays essential roles in lipid transport to construct the lipid barrier of insect cuticles and in pesticide detoxification within insects. Since ABCH transporters are critical for all insects but absent in mammals and plants, this elegant and exciting work provides a highly promising target for developing safe, low-resistance pesticides. Here, we highlight the groundbreaking discoveries made by Qing Yang's team in unraveling the intricate mechanisms of the ABCH transporter.展开更多
Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in ...Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.展开更多
Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit informati...Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit information,a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model(SNID-ENSEF)is proposed.Syntactic elimination contrast learning leverages verb-dominant syntactic features,systematically replacing specific words to enhance data diversity.The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency.A neighborhood sample fusion strategy,based on sample distribution patterns,dynamically adjusts neighborhood size and fuses sample vectors to reduce noise and improve implicit information utilization and discovery accuracy.Experimental results show that SNID-ENSEF achieves average improvements of 0.88%,1.27%,and 1.30%in Normalized Mutual Information(NMI),Accuracy(ACC),and Adjusted Rand Index(ARI),respectively,outperforming PTJN,DPN,MTP-CLNN,and DWG models on the Banking77,StackOverflow,and Clinc150 datasets.The code is available at https://github.com/qsdesz/SNID-ENSEF,accessed on 16 January 2025.展开更多
Existing text truth discovery methods fail to address two challenges:the inherent long-distance dependencies and thematic diversity of long texts;the inherent subjective sentiment that obscures objective evaluation of...Existing text truth discovery methods fail to address two challenges:the inherent long-distance dependencies and thematic diversity of long texts;the inherent subjective sentiment that obscures objective evaluation of source reliability.To address these challenges,a novel truth discovery method named large language model(LLM)-enhanced text truth discovery with dual attention(LTDDA)is proposed.First,LLMs generate embedded representations of text claims,and enhance the feature space to tackle long-distance dependencies and thematic diversity.Then,the complex relationship between source reliability and claim credibility is captured by integrating semantic and sentiment features.Finally,dual-layer attention is applied to extract key semantic information and assign consistent weights to similar sources,resulting in accurate truth outputs.Extensive experiments on three realworld datasets demonstrate that the effectiveness of LTDDA outperforms that of state-of-the-art methods,providing new insights for building more reliable and accurate text truth discovery systems.展开更多
The 3-（4,5-dimethylthiazol-2-yl）-2,5-diphenyl tetrazolium bromide （MTT） assay is a routine cell viability assay for cell proliferation and cytotoxicity, which is widely used in many fields, especially in screening...The 3-（4,5-dimethylthiazol-2-yl）-2,5-diphenyl tetrazolium bromide （MTT） assay is a routine cell viability assay for cell proliferation and cytotoxicity, which is widely used in many fields, especially in screening for drug discovery. However, this assay exhibits limitations in the presence of particular compounds and under certain assay conditions, which may yield false screening results. For example, polyphenols that are extracted from natural sources can react with MTT in the absence of living cells and thus interfere with the screening results. We measured the absorbance of 15 polyphenols extracted from green tea and showed that the phenolic hydroxyl groups in the polyphenols are responsible for the reduction of MTT to formazan. When three or more phenolic hydroxyl groups were present on a conjugated polyphenol, a significantly increased MTT reduction was observed. Moreover, the type of medium also had an effect on the absorbance value, in the following order： ct-MEM ＋ 10% FBS〉 a-MEM〉DMEM/F12〉PBS. The absorbance of the MTT assay recorded at 570 nm is more sensitive than that measured at 595 nm. These results will improve the cell-based assay of polyphenols and clarify the limitations of the MTT assay as a method of screening in drug discovery.展开更多
非键相互作用对于生物体系中的分子识别和结合过程起着关键作用。然而,传统的方法并不能在残基水平自动批量计算非键相互作用。近年来,已经发展了一些方法和工具进行非键相互作用的计算分析。该文研究发展了一种可以自动计算残基间非键...非键相互作用对于生物体系中的分子识别和结合过程起着关键作用。然而,传统的方法并不能在残基水平自动批量计算非键相互作用。近年来,已经发展了一些方法和工具进行非键相互作用的计算分析。该文研究发展了一种可以自动计算残基间非键相互作用的方法,即用Perl脚本调用Discovery Studio 2.0(DS 2.0,Accelrys Inc.)底层模块中的非键相互作用协议,实现了直接利用命令行批量计算非键相互作用能量,而无需通过DS2.0的图形界面。该方法扩展了DS2.0的计算模块,并于近期运用到了复合结构的研究分析中。展开更多
文摘Drug discovery is a complex and highly systematic process encompassing multiple critical stages,including target identification,bioactive molecule discovery,preclinical research,clinical trials,regulatory review,post-marketing surveillance,and others[1].This process typically spans many years and is often accompanied by high failure rates and substantial resource consumption.In recent years,driven by large amounts of biomedical data,artificial intelligence(AI)has begun to reshape every stage of drug discovery[2].Particularly,by integrating diverse,high-dimensional datasets with powerful predictive and generative models.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金supported by National Natural Science Foundation of China(82341087,82073912,and 81903896)a project funded by Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.
文摘Anemoside B4(AB4),a triterpenoidal saponin derived from Pulsatilla chinensis,has garnered considerable attention for its potent anti-inflammatory and immunomodulatory activities,culminating in its approval for clinical trials by the Center for Drug Evaluation,National Medical Products Administration,for the treatment of mild to moderate ulcerative colitis.Despite this,AB4’s therapeutic potential remained underexplored until the development of its injection formulation.This review discusses the scientific rationale and theoretical framework behind AB4’s development,offering a new paradigm and innovative research strategy for discovering lead compounds or drug candidates from natural medicines.In-depth investigations into AB4’s cellular targets,biochemical pathways,and administration routes have provided valuable insights into its druggability evaluation and clinical potential.The high water solubility of AB4,attributable to its multiple sugar units,imposes limitations on its bioavailability and pharmacokinetic profiles.To address this,structural modification via chemical methods and enzymatic hydrolysis have been employed,resulting in derivatives with reduced molecular weight,improved bioavailability,enhanced pharmacological activity,and greater clinical potential.These advances lay a solid foundation for the continued development of AB4 and its derivatives as promising therapeutic agents.
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
基金supported by grants from the National Natural Science Foundation of China(No.82273770)the Foundation for Innovative Research Groups of the National Natural Science Foundation of Sichuan Province(No.24NSFTD0051).
文摘In the realm of drug discovery,recent advancements have paved the way for innovative approaches and methodologies.This comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light on novel strategies in drug development.(a)The resurgence of covalent drugs is highlighted,focusing on the targeted covalent inhibitors(TCIs)and their role in enhancing selectivity and affinity.(b)The potential of the quantum mechanics-based computational aid drug design(CADD)tool,Cov_DOX,is introduced for predicting protein-covalent ligand binding structures and affinities.(c)The scaffolding function of proteins is proposed as a new avenue for drug design,with a focus on modulating protein-protein interactions through small molecules and proteolysis targeting chimeras(PROTACs).(d)The concept of pro-PROTACs is explored as a promising strategy for cancer therapy,combining the principles of prodrugs and PROTACs to enhance specificity and reduce toxicity.(e)The design of prodrugs through carbon-carbon bond cleavage is discussed,offering a new perspective for the activation of drugs with limited modifiable functional groups.(f)The targeting of programmed cell death pathways in cancer therapies with small molecules is reviewed,emphasizing the induction of autophagy-dependent cell death,ferroptosis,and cuproptosis.These insights collectively contribute to a deeper understanding of the dynamic landscape of drug discovery.
文摘Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to accomplish industrial purposes.Systems biology seeks to understand biology at multiple dimensions,beginning with the molecular and cellular level and progressing to the tissues and organismal level and characterizes cells as complex information-processing systems.SynBio,on the other hand,toggles further and strives to develop and create its systems from scratch.SynBio is now applied in the development of novel therapeutic drugs for the prevention of human diseases,scale up industrial processes,and accomplish previously unfeasible industrial outcomes.This is made possible through significant breakthroughs in DNA sequencing and synthesis technology,as well as insights gained from synthetic chemistry and systems biology.SynBio technologies have allowed for the introduction of improved and synthetic metabolic functionalities in microorganisms to enable the synthesis of a range of pharmacologically-relevant compounds for pharmaceutical exploration.SynBio applications range from finding new ways to making industrial chemical synthesis processes more sustainable as well as the microbial synthesis of improved therapeutic modalities.Hence,this study underpins several innovations,auspicious potentials,and future directions afforded by SynBio that proposes improved industrial microbial synthesis for pharmaceutical exploration.
文摘Endometrial cancer is the most common gynecologic cancer diagnosed in the United States and mortality is on the rise.Advanced and recurrent endometrial cancer represents a treatment challenge as historically there have been limited therapeutic options for patients.In the last several years,multiple practice-changing clinical trials have led to significant improvements in the treatment landscape.This review will cover updates in the treatment and management of advanced and recurrent endometrial cancer with a focus on novel therapeutics,such as anti-PD-L1 and PD-1 inhibitors,poly ADP-ribose polymerase(PARP)inhibitors,antibody-drug conjugates,and hormonal therapy.
基金supported by the National Social Science Fund of China(2022-SKJJ-B-084).
文摘The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.
基金the support of the National Natural Science Foundation of China(Nos.22205207 and 22378369).
文摘Zeolites are crystalline microporous materials widely used in catalysis,adsorption,and ion exchange owing to their tunable pore structures and acid centers[1].Traditional zeolites,however,often suffer from limitations such as restricted molecular diffusion and rapid coking,which hinder their efficiency in processing large molecules.
基金supported by the National Key R&D Program of China(Grant No.:2023YFF1205102)the National Natural Science Foundation of China(Grant Nos.:82273856,22077143,and 21977127)the Science Foundation of Guangzhou,China(No.:2Grant024A04J2172).
文摘Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.
基金National Natural Science Foundation of China(32471265).
文摘Pesticides play a pivotal role in modern agriculture. However, the pesticide industry faces significant challenges closely linked to major global concerns such as pesticide resistance, environmental pollution, food safety, and crop yields. Developing safe, efficient, and environmentally friendly pesticides has become a key challenge for the industry. Recently, Qing Yang and colleagues unveiled the mode of action of a dual-functional protein, the ABCH transporter, which plays essential roles in lipid transport to construct the lipid barrier of insect cuticles and in pesticide detoxification within insects. Since ABCH transporters are critical for all insects but absent in mammals and plants, this elegant and exciting work provides a highly promising target for developing safe, low-resistance pesticides. Here, we highlight the groundbreaking discoveries made by Qing Yang's team in unraveling the intricate mechanisms of the ABCH transporter.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFA1402304)the National Natural Science Foundation of China(Grant Nos.12034009,12374005,52288102,52090024,and T2225013)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for JLU Science and Technology Innovative Research Team.
文摘Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.
基金supported by Research Projects of the Nature Science Foundation of Hebei Province(F2021402005).
文摘Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit information,a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model(SNID-ENSEF)is proposed.Syntactic elimination contrast learning leverages verb-dominant syntactic features,systematically replacing specific words to enhance data diversity.The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency.A neighborhood sample fusion strategy,based on sample distribution patterns,dynamically adjusts neighborhood size and fuses sample vectors to reduce noise and improve implicit information utilization and discovery accuracy.Experimental results show that SNID-ENSEF achieves average improvements of 0.88%,1.27%,and 1.30%in Normalized Mutual Information(NMI),Accuracy(ACC),and Adjusted Rand Index(ARI),respectively,outperforming PTJN,DPN,MTP-CLNN,and DWG models on the Banking77,StackOverflow,and Clinc150 datasets.The code is available at https://github.com/qsdesz/SNID-ENSEF,accessed on 16 January 2025.
文摘Existing text truth discovery methods fail to address two challenges:the inherent long-distance dependencies and thematic diversity of long texts;the inherent subjective sentiment that obscures objective evaluation of source reliability.To address these challenges,a novel truth discovery method named large language model(LLM)-enhanced text truth discovery with dual attention(LTDDA)is proposed.First,LLMs generate embedded representations of text claims,and enhance the feature space to tackle long-distance dependencies and thematic diversity.Then,the complex relationship between source reliability and claim credibility is captured by integrating semantic and sentiment features.Finally,dual-layer attention is applied to extract key semantic information and assign consistent weights to similar sources,resulting in accurate truth outputs.Extensive experiments on three realworld datasets demonstrate that the effectiveness of LTDDA outperforms that of state-of-the-art methods,providing new insights for building more reliable and accurate text truth discovery systems.
基金National Natural Science Foundation of China (Grant No.30672491)Beijing New Medical Discipline Based Group(Grant No.XK 100270569)
文摘The 3-（4,5-dimethylthiazol-2-yl）-2,5-diphenyl tetrazolium bromide （MTT） assay is a routine cell viability assay for cell proliferation and cytotoxicity, which is widely used in many fields, especially in screening for drug discovery. However, this assay exhibits limitations in the presence of particular compounds and under certain assay conditions, which may yield false screening results. For example, polyphenols that are extracted from natural sources can react with MTT in the absence of living cells and thus interfere with the screening results. We measured the absorbance of 15 polyphenols extracted from green tea and showed that the phenolic hydroxyl groups in the polyphenols are responsible for the reduction of MTT to formazan. When three or more phenolic hydroxyl groups were present on a conjugated polyphenol, a significantly increased MTT reduction was observed. Moreover, the type of medium also had an effect on the absorbance value, in the following order： ct-MEM ＋ 10% FBS〉 a-MEM〉DMEM/F12〉PBS. The absorbance of the MTT assay recorded at 570 nm is more sensitive than that measured at 595 nm. These results will improve the cell-based assay of polyphenols and clarify the limitations of the MTT assay as a method of screening in drug discovery.
文摘非键相互作用对于生物体系中的分子识别和结合过程起着关键作用。然而,传统的方法并不能在残基水平自动批量计算非键相互作用。近年来,已经发展了一些方法和工具进行非键相互作用的计算分析。该文研究发展了一种可以自动计算残基间非键相互作用的方法,即用Perl脚本调用Discovery Studio 2.0(DS 2.0,Accelrys Inc.)底层模块中的非键相互作用协议,实现了直接利用命令行批量计算非键相互作用能量,而无需通过DS2.0的图形界面。该方法扩展了DS2.0的计算模块,并于近期运用到了复合结构的研究分析中。
