Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of dr...Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.展开更多
Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been de...Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.展开更多
Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes t...Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence-based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping-based, molecular distribution matching-based, and guided search-based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence-based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.展开更多
Recent publications reveal the disturbing information that a minor edit to an algorithm being used for designing legitimate drug candidates redirected the program in a way that resulted in the surprising design of nov...Recent publications reveal the disturbing information that a minor edit to an algorithm being used for designing legitimate drug candidates redirected the program in a way that resulted in the surprising design of novel chemical warfare agent candidates. Although this outcome was not the result of nefarious intent, and appropriate chemical defense authorities were notified, the potential implications of some misapplication of a drug-design algorithm for nefarious purposes are clear. This Commentary summarizes how otherwise benign Artificial Intelligence (AI) algorithms used for drug discovery can be easily reversed to design novel chemical warfare agents for which no effective antidote will be available, or perhaps even envisioned.展开更多
The rapidly advancing field of artificial intelligence(AI)has garnered substantial attention for its potential application in drug discovery and development.This opinion review critically examined the feasibility and ...The rapidly advancing field of artificial intelligence(AI)has garnered substantial attention for its potential application in drug discovery and development.This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry.AI,encompassing machine learning algorithms,deep learning,and data analytics,offers unprecedented opportunities to streamline and enhance various stages of drug development.This opinion review delved into the current landscape of AI-driven approaches,discussing their utilization in target identification,lead optimization,and predictive modeling of pharmacokinetics and toxicity.We aimed to scrutinize the integration of large-scale omics data,electronic health records,and chemical informatics,highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies.Despite the considerable potential of AI,the review also addressed inherent challenges,including data privacy concerns,interpretability of AI models,and the need for robust validation in realworld clinical settings.Additionally,we explored ethical considerations surrounding AI-driven decision-making in drug development.This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends,presenting critical insights and addressing potential hurdles.In conclusion,this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.展开更多
G protein-coupled receptors(GPCRs)are crucial players in various physiological processes,making them attractive candidates for drug discovery.However,traditional approaches to GPCR ligand discovery are time-consuming ...G protein-coupled receptors(GPCRs)are crucial players in various physiological processes,making them attractive candidates for drug discovery.However,traditional approaches to GPCR ligand discovery are time-consuming and resource-intensive.The emergence of artificial intelligence(AI)methods has revolutionized the field of GPCR ligand discovery and has provided valuable tools for accelerating the identification and optimization of GPCR ligands.In this study,we provide guidelines for effectively utilizing AI methods for GPCR ligand discovery,including data collation and representation,model selection,and specific applications.First,the online resources that are instrumental in GPCR ligand discovery were summarized,including databases and repositories that contain valuable GPCR-related information and ligand data.Next,GPCR and ligand representation schemes that can convert data into computer-readable formats were introduced.Subsequently,the key applications of AI methods in the different stages of GPCR drug discovery were discussed,ranging from GPCR function prediction to ligand design and agonist identification.Furthermore,an AI-driven multi-omics integration strategy for GPCR ligand discovery that combines information from various omics disciplines was proposed.Finally,the challenges and future directions of the application of AI in GPCR research were deliberated.In conclusion,continued advancements in AI techniques coupled with interdisciplina ry collaborations will offer great potential for improving the efficiency of GPCR ligand discovery.展开更多
The current rise of artificial intelligence and machine learning has been significant.It has reduced the human workload improved quality of life significantly.This article describes the use of artificial intelligence ...The current rise of artificial intelligence and machine learning has been significant.It has reduced the human workload improved quality of life significantly.This article describes the use of artificial intelligence and machine learning to augment drug discovery and development to make them more efficient and accurate.In this study,a systematic evaluation of studies was carried out;these were selected based on prior knowledge of the authors and a keyword search in publicly available databases which were filtered based on related context,abstract,methodology,and full text.This body of work supported the roles of machine learning and artificial intelligence in facilitating drug development and discovery processes,making them more cost-effective or altogether eliminating the need for clinical trials,owing to the ability to conduct simulations using these technologies.They also enabled researchers to study different molecules more extensively,without any trials.The results of this paper demonstrate the prevalent application of machine learning and artificial intelligence methods in drug discovery,and indicate a promising future for these technologies;these results should enable researchers,students,and pharmaceutical industry to dive deeper into machine learning and artificial intelligence in a drug discovery and development context.展开更多
基金funded by the Natural Science Foundation of Zhejiang Province(LR21H300001)National Key R&D Program of China(2022YFC3400501)+4 种基金National Natural Science Foundation of China(22220102001,U1909208,81872798,and 81825020)Leading Talent of the“Ten Thousand Plan”-National High-Level Talents Special Support Plan of ChinaFundamental Research Fund of Central University(2018QNA7023)Key R&D Program of Zhejiang Province(2020C03010)“Double Top-Class”University(181201*194232101)。
文摘Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.
基金funded by the National Natural Science Foundation of China (NSFC, 31900046, 81972085, 82172465 and 32161133022)the Guangdong Provincial Key Laboratory of Advanced Biomaterials (2022B1212010003)+7 种基金the National Science and Technology Innovation 2030 Major Program (2022ZD0211900)the Shenzhen Key Laboratory of Computer Aided Drug Discovery (ZDSYS20201230165400001)the Chinese Academy of Science President’s International Fellowship Initiative (PIFI)(2020FSB0003)the Guangdong Retired Expert (granted by Guangdong Province)the Shenzhen Pengcheng ScientistNSFC-SNSF Funding (32161133022)Alpha Mol&SIAT Joint LaboratoryShenzhen Government Top-talent Working Funding and Guangdong Province Academician Work Funding。
文摘Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.
基金The National Natural Science Foundation of China,Grant/Award Numbers:62372204,62072206,62102158,61772381the Fundamental Research Funds for the Central Universities,Grant/Award Numbers:2662022JC004,2662021JC008。
文摘Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence-based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping-based, molecular distribution matching-based, and guided search-based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence-based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.
文摘Recent publications reveal the disturbing information that a minor edit to an algorithm being used for designing legitimate drug candidates redirected the program in a way that resulted in the surprising design of novel chemical warfare agent candidates. Although this outcome was not the result of nefarious intent, and appropriate chemical defense authorities were notified, the potential implications of some misapplication of a drug-design algorithm for nefarious purposes are clear. This Commentary summarizes how otherwise benign Artificial Intelligence (AI) algorithms used for drug discovery can be easily reversed to design novel chemical warfare agents for which no effective antidote will be available, or perhaps even envisioned.
基金Supported by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,No.BG-RRP-2.004-0008.
文摘The rapidly advancing field of artificial intelligence(AI)has garnered substantial attention for its potential application in drug discovery and development.This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry.AI,encompassing machine learning algorithms,deep learning,and data analytics,offers unprecedented opportunities to streamline and enhance various stages of drug development.This opinion review delved into the current landscape of AI-driven approaches,discussing their utilization in target identification,lead optimization,and predictive modeling of pharmacokinetics and toxicity.We aimed to scrutinize the integration of large-scale omics data,electronic health records,and chemical informatics,highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies.Despite the considerable potential of AI,the review also addressed inherent challenges,including data privacy concerns,interpretability of AI models,and the need for robust validation in realworld clinical settings.Additionally,we explored ethical considerations surrounding AI-driven decision-making in drug development.This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends,presenting critical insights and addressing potential hurdles.In conclusion,this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.
基金This work was supported by the National Science and Technology Major Project(2022ZD0115003)the National Natural Science Foundation of China(No.92053202,No.92353304,No.22050003,No.21821004,No.21927901).
基金Natural Science Foundation of Sichuan(2023NSFSC0683)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTD-D202209).
文摘G protein-coupled receptors(GPCRs)are crucial players in various physiological processes,making them attractive candidates for drug discovery.However,traditional approaches to GPCR ligand discovery are time-consuming and resource-intensive.The emergence of artificial intelligence(AI)methods has revolutionized the field of GPCR ligand discovery and has provided valuable tools for accelerating the identification and optimization of GPCR ligands.In this study,we provide guidelines for effectively utilizing AI methods for GPCR ligand discovery,including data collation and representation,model selection,and specific applications.First,the online resources that are instrumental in GPCR ligand discovery were summarized,including databases and repositories that contain valuable GPCR-related information and ligand data.Next,GPCR and ligand representation schemes that can convert data into computer-readable formats were introduced.Subsequently,the key applications of AI methods in the different stages of GPCR drug discovery were discussed,ranging from GPCR function prediction to ligand design and agonist identification.Furthermore,an AI-driven multi-omics integration strategy for GPCR ligand discovery that combines information from various omics disciplines was proposed.Finally,the challenges and future directions of the application of AI in GPCR research were deliberated.In conclusion,continued advancements in AI techniques coupled with interdisciplina ry collaborations will offer great potential for improving the efficiency of GPCR ligand discovery.
文摘The current rise of artificial intelligence and machine learning has been significant.It has reduced the human workload improved quality of life significantly.This article describes the use of artificial intelligence and machine learning to augment drug discovery and development to make them more efficient and accurate.In this study,a systematic evaluation of studies was carried out;these were selected based on prior knowledge of the authors and a keyword search in publicly available databases which were filtered based on related context,abstract,methodology,and full text.This body of work supported the roles of machine learning and artificial intelligence in facilitating drug development and discovery processes,making them more cost-effective or altogether eliminating the need for clinical trials,owing to the ability to conduct simulations using these technologies.They also enabled researchers to study different molecules more extensively,without any trials.The results of this paper demonstrate the prevalent application of machine learning and artificial intelligence methods in drug discovery,and indicate a promising future for these technologies;these results should enable researchers,students,and pharmaceutical industry to dive deeper into machine learning and artificial intelligence in a drug discovery and development context.
