Marine terpenoids are a structurally diverse class of natural products produced by marine organisms,characterized by unique molecular architectures and notable biological activities.They play essential roles in ecolog...Marine terpenoids are a structurally diverse class of natural products produced by marine organisms,characterized by unique molecular architectures and notable biological activities.They play essential roles in ecological interactions and chemical defense,while also exhibiting promising therapeutic properties,including anti-inflammatory and anti-tumor effects.In this review,we compile 13132 reported marine terpenoids,of which 2066 have documented biological activities,and provide a concise summary of their organismal origins,molecular scaffolds,and associated activities.Cheminformatics approaches are further applied to compare the chemical space of marine versus terrestrial terpenoids,highlighting their structural distinctiveness.Finally,we discuss promising directions for the discovery,utilization,and synthesis of marine terpenoids,with the goal of promoting comprehensive and sustainable exploration of these valuable marine resources.展开更多
Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug developme...Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process.Recent advancements in artificial intelligence(AI),particularly in machine learning(ML)and deep learning(DL)techniques,have introduced innovative approaches to metabolism research,enabling more accurate predictions and insights.This paper emphasizes computational and AI-driven methodologies,highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models(GEMs)at the omics level.Challenges still remain,including data heterogeneity and model interpretability.This work aims to provide valuable insights and references for researchers in drug discovery and development,ultimately contributing to the advancement of precision medicine.展开更多
Computer-assisted chemical structure searching plays a critical role for efficient structure screening in cheminformatics. We designed a high-performance chemical structure & data search engine called DCAIKU, buil...Computer-assisted chemical structure searching plays a critical role for efficient structure screening in cheminformatics. We designed a high-performance chemical structure & data search engine called DCAIKU, built on CouchDB and ElasticSearch engines. DCAIKU converts the chemical structure similarity search problem into a general text search problem to utilize off-the-shelf full-text search engines. DCAIKU also supports flexible document structures and heterogeneous datasets with the help of schema-less document database. Our evaluations show that DCAIKU can handle both keyword search and structural search against millions of records with both high accuracy and low latency. We expect that DCAIKU will lay the foundation towards large-scale and cost-effective structural search in materials science and chemistry research.展开更多
Medicinal Organometallic Chemistry keeps contributing to drug discovery efforts including the development of diagnostic compounds. Despite the limiting issues of metal-based molecules, e.g., such as toxicity, there ar...Medicinal Organometallic Chemistry keeps contributing to drug discovery efforts including the development of diagnostic compounds. Despite the limiting issues of metal-based molecules, e.g., such as toxicity, there are drugs approved for clinical use and several others are under clinical and pre-clinical development. Indeed, several research groups continue working on organometallic compounds with potential therapeutic applications. For arguably historical reasons, chemoinformatic methods in drug discovery have been applied thus far mostly to organic compounds. Typically, metal-based molecules are excluded from compound data sets for analysis. Indeed, most software and algorithms for drug discovery applications are focused and parametrized for organic molecules. However, considering the emerging field of material informatics, the objective of this Commentary we emphasize the need to develop cheminformatic applications to further develop metallodrugs. For instance, one of the starting points would be developing a compound database of organometallic molecules annotated with biological activity. It is concluded that chemoinformatic methods can boost the research area of Medicinal Organometallic Chemistry.展开更多
Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learnin...Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.展开更多
The polar surface area of a molecule is currently defined as the surface sum over all polar atoms, primarily oxygen and nitrogen, also including their attached hydrogens (named PSA1 in the present study). Some authors...The polar surface area of a molecule is currently defined as the surface sum over all polar atoms, primarily oxygen and nitrogen, also including their attached hydrogens (named PSA1 in the present study). Some authors also include sulfur and phosphor (PSA3). The slight modification suggested here is based on the fact that it is difficult to consider, on a theoretical point of view, hexavalent S and pentavalents N and P as polar atoms. Indeed, in these cases, all their peripheral electrons are involved in bondings. We propose to define PSA2 using the initial definition extended to O, S, N, P, with the exception of hexavalent S and pentavalents N and P. In order to test this hypothesis, the three expressions PSA1, PSA2 and PSA3 have been applied in a QSAR to a physiological phenomenon called comfort olfactory perceived intensity, for the human responses to 186 odorants (QSAR stands for Quantitative Structure Activity Relationship). The PSA2 expression has been selected as the more suitable, associated with two other molecular properties (molar refraction and Van der Waals molecular volume).展开更多
The integration of artificial intelligence(AI)and machine learning(ML)into pharmaceutical sciences has catalyzed transformative advancements across drug discovery,clinical development,manufacturing,and post-market sur...The integration of artificial intelligence(AI)and machine learning(ML)into pharmaceutical sciences has catalyzed transformative advancements across drug discovery,clinical development,manufacturing,and post-market surveillance.This review comprehensively examines AI's role in modern pharmacotherapy,beginning with its historical evolution in life sciences and progressing to cutting-edge applications such as AlphaFold-driven protein modeling,natural language processing(NLP)for biomedical literature mining,and AI-augmented pharmacovigilance.Methodologically,we synthesize interdisciplinary insights from peer-reviewed literature(2013-2023),highlighting innovations in cheminformatics(e.g.,QSAR,RDKit),predictive toxi-cology,and personalized medicine.Case studies illustrate AI's capacity to compress drug development timelines,as seen in COVID-19 repurposing efforts and de novo kinase inhibitor design.However,challenges persist,including algorithmic bias,regulatory ambiguities,and the“black-box”nature of deep learning models.By critically evaluating successes and limitations,this review underscores AI's potential to redefine pharmaceutical innovation while advocating for robust frameworks to ensure ethical,transparent,and clinically translatable AI deployment.展开更多
基金supported by the National Natural Science Foundation of China(Nos.22473118 and 82430108)the National Key Research and Development Program of China(No.2023YFC3404900)the Top-Notch Young Talents Program of China for its support。
文摘Marine terpenoids are a structurally diverse class of natural products produced by marine organisms,characterized by unique molecular architectures and notable biological activities.They play essential roles in ecological interactions and chemical defense,while also exhibiting promising therapeutic properties,including anti-inflammatory and anti-tumor effects.In this review,we compile 13132 reported marine terpenoids,of which 2066 have documented biological activities,and provide a concise summary of their organismal origins,molecular scaffolds,and associated activities.Cheminformatics approaches are further applied to compare the chemical space of marine versus terrestrial terpenoids,highlighting their structural distinctiveness.Finally,we discuss promising directions for the discovery,utilization,and synthesis of marine terpenoids,with the goal of promoting comprehensive and sustainable exploration of these valuable marine resources.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.:82425104 and 82173690)the National Key R&D Program of China(Grant Nos:2022YFC3400501 and 2022YFC3400504)the Shanghai Rising-Star Program,China(Grant No:23QA1402800).
文摘Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process.Recent advancements in artificial intelligence(AI),particularly in machine learning(ML)and deep learning(DL)techniques,have introduced innovative approaches to metabolism research,enabling more accurate predictions and insights.This paper emphasizes computational and AI-driven methodologies,highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models(GEMs)at the omics level.Challenges still remain,including data heterogeneity and model interpretability.This work aims to provide valuable insights and references for researchers in drug discovery and development,ultimately contributing to the advancement of precision medicine.
基金This work was supported by the National Natural Science Foundation of China,the Ministry of Science and Technology of China,and the Swedish Research Council.
文摘Computer-assisted chemical structure searching plays a critical role for efficient structure screening in cheminformatics. We designed a high-performance chemical structure & data search engine called DCAIKU, built on CouchDB and ElasticSearch engines. DCAIKU converts the chemical structure similarity search problem into a general text search problem to utilize off-the-shelf full-text search engines. DCAIKU also supports flexible document structures and heterogeneous datasets with the help of schema-less document database. Our evaluations show that DCAIKU can handle both keyword search and structural search against millions of records with both high accuracy and low latency. We expect that DCAIKU will lay the foundation towards large-scale and cost-effective structural search in materials science and chemistry research.
文摘Medicinal Organometallic Chemistry keeps contributing to drug discovery efforts including the development of diagnostic compounds. Despite the limiting issues of metal-based molecules, e.g., such as toxicity, there are drugs approved for clinical use and several others are under clinical and pre-clinical development. Indeed, several research groups continue working on organometallic compounds with potential therapeutic applications. For arguably historical reasons, chemoinformatic methods in drug discovery have been applied thus far mostly to organic compounds. Typically, metal-based molecules are excluded from compound data sets for analysis. Indeed, most software and algorithms for drug discovery applications are focused and parametrized for organic molecules. However, considering the emerging field of material informatics, the objective of this Commentary we emphasize the need to develop cheminformatic applications to further develop metallodrugs. For instance, one of the starting points would be developing a compound database of organometallic molecules annotated with biological activity. It is concluded that chemoinformatic methods can boost the research area of Medicinal Organometallic Chemistry.
文摘Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.
文摘The polar surface area of a molecule is currently defined as the surface sum over all polar atoms, primarily oxygen and nitrogen, also including their attached hydrogens (named PSA1 in the present study). Some authors also include sulfur and phosphor (PSA3). The slight modification suggested here is based on the fact that it is difficult to consider, on a theoretical point of view, hexavalent S and pentavalents N and P as polar atoms. Indeed, in these cases, all their peripheral electrons are involved in bondings. We propose to define PSA2 using the initial definition extended to O, S, N, P, with the exception of hexavalent S and pentavalents N and P. In order to test this hypothesis, the three expressions PSA1, PSA2 and PSA3 have been applied in a QSAR to a physiological phenomenon called comfort olfactory perceived intensity, for the human responses to 186 odorants (QSAR stands for Quantitative Structure Activity Relationship). The PSA2 expression has been selected as the more suitable, associated with two other molecular properties (molar refraction and Van der Waals molecular volume).
文摘The integration of artificial intelligence(AI)and machine learning(ML)into pharmaceutical sciences has catalyzed transformative advancements across drug discovery,clinical development,manufacturing,and post-market surveillance.This review comprehensively examines AI's role in modern pharmacotherapy,beginning with its historical evolution in life sciences and progressing to cutting-edge applications such as AlphaFold-driven protein modeling,natural language processing(NLP)for biomedical literature mining,and AI-augmented pharmacovigilance.Methodologically,we synthesize interdisciplinary insights from peer-reviewed literature(2013-2023),highlighting innovations in cheminformatics(e.g.,QSAR,RDKit),predictive toxi-cology,and personalized medicine.Case studies illustrate AI's capacity to compress drug development timelines,as seen in COVID-19 repurposing efforts and de novo kinase inhibitor design.However,challenges persist,including algorithmic bias,regulatory ambiguities,and the“black-box”nature of deep learning models.By critically evaluating successes and limitations,this review underscores AI's potential to redefine pharmaceutical innovation while advocating for robust frameworks to ensure ethical,transparent,and clinically translatable AI deployment.
