This work presents the authors＇ experience in the field of mobile technologies, from which several initiatives have emerged. As result of this, a games-based framework for learning has been developed in these last ye...This work presents the authors＇ experience in the field of mobile technologies, from which several initiatives have emerged. As result of this, a games-based framework for learning has been developed in these last years. This framework is composed by a competition called Mobigame, which has as main aim to stimulate the participation of the students. By participating in this competition participants learn to develop for mobile devices. A game to practice Japanese is also presented in this article, which was presented in the above mentioned competition. This game has been developed for mobile phones or PDAs （Personal Digital Assistants） based on the JME （Java Mobile Edition） technology. Finally, another initiative is also presented： A free download platform of digital contents for mobile devices based on info-educational games.展开更多
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage...Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.展开更多
文摘This work presents the authors＇ experience in the field of mobile technologies, from which several initiatives have emerged. As result of this, a games-based framework for learning has been developed in these last years. This framework is composed by a competition called Mobigame, which has as main aim to stimulate the participation of the students. By participating in this competition participants learn to develop for mobile devices. A game to practice Japanese is also presented in this article, which was presented in the above mentioned competition. This game has been developed for mobile phones or PDAs （Personal Digital Assistants） based on the JME （Java Mobile Edition） technology. Finally, another initiative is also presented： A free download platform of digital contents for mobile devices based on info-educational games.
基金supported by the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81773634 to M.Z. and 81430084 to K.C.)the “Personalized Medicines-Molecular Signature-based Drug Discovery and Development”, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.)+1 种基金National Key Research & Development Plan (2016YFC1201003 to M.Z.)the National Basic Research Program (2015CB910304 to X.L.)
文摘Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.
