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...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 con-verts the chemical structure similarity search problem into a general text search problem to utilize off-the-shelf full-text search engines.DCAIKU also supports exible document struc-tures and heterogeneous datasets with the help of schema-less document database.Our eval-uations 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
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 con-verts the chemical structure similarity search problem into a general text search problem to utilize off-the-shelf full-text search engines.DCAIKU also supports exible document struc-tures and heterogeneous datasets with the help of schema-less document database.Our eval-uations 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).
