7,697 research outputs found
In silico generation of novel, drug-like chemical matter using the LSTM neural network
The exploration of novel chemical spaces is one of the most important tasks
of cheminformatics when supporting the drug discovery process. Properly
designed and trained deep neural networks can provide a viable alternative to
brute-force de novo approaches or various other machine-learning techniques for
generating novel drug-like molecules. In this article we present a method to
generate molecules using a long short-term memory (LSTM) neural network and
provide an analysis of the results, including a virtual screening test. Using
the network one million drug-like molecules were generated in 2 hours. The
molecules are novel, diverse (contain numerous novel chemotypes), have good
physicochemical properties and have good synthetic accessibility, even though
these qualities were not specific constraints. Although novel, their structural
features and functional groups remain closely within the drug-like space
defined by the bioactive molecules from ChEMBL. Virtual screening using the
profile QSAR approach confirms that the potential of these novel molecules to
show bioactivity is comparable to the ChEMBL set from which they were derived.
The molecule generator written in Python used in this study is available on
request.Comment: in this version fixed some reference number
Enabling High-Level Application Development for the Internet of Things
Application development in the Internet of Things (IoT) is challenging
because it involves dealing with a wide range of related issues such as lack of
separation of concerns, and lack of high-level of abstractions to address both
the large scale and heterogeneity. Moreover, stakeholders involved in the
application development have to address issues that can be attributed to
different life-cycles phases. when developing applications. First, the
application logic has to be analyzed and then separated into a set of
distributed tasks for an underlying network. Then, the tasks have to be
implemented for the specific hardware. Apart from handling these issues, they
have to deal with other aspects of life-cycle such as changes in application
requirements and deployed devices. Several approaches have been proposed in the
closely related fields of wireless sensor network, ubiquitous and pervasive
computing, and software engineering in general to address the above challenges.
However, existing approaches only cover limited subsets of the above mentioned
challenges when applied to the IoT. This paper proposes an integrated approach
for addressing the above mentioned challenges. The main contributions of this
paper are: (1) a development methodology that separates IoT application
development into different concerns and provides a conceptual framework to
develop an application, (2) a development framework that implements the
development methodology to support actions of stakeholders. The development
framework provides a set of modeling languages to specify each development
concern and abstracts the scale and heterogeneity related complexity. It
integrates code generation, task-mapping, and linking techniques to provide
automation. Code generation supports the application development phase by
producing a programming framework that allows stakeholders to focus on the
application logic, while our mapping and linking techniques together support
the deployment phase by producing device-specific code to result in a
distributed system collaboratively hosted by individual devices. Our evaluation
based on two realistic scenarios shows that the use of our approach improves
the productivity of stakeholders involved in the application development
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
In recent years, ubiquitous semantic Metaverse has been studied to
revolutionize immersive cyber-virtual experiences for augmented reality (AR)
and virtual reality (VR) users, which leverages advanced semantic understanding
and representation to enable seamless, context-aware interactions within
mixed-reality environments. This survey focuses on the intelligence and
spatio-temporal characteristics of four fundamental system components in
ubiquitous semantic Metaverse, i.e., artificial intelligence (AI),
spatio-temporal data representation (STDR), semantic Internet of Things (SIoT),
and semantic-enhanced digital twin (SDT). We thoroughly survey the
representative techniques of the four fundamental system components that enable
intelligent, personalized, and context-aware interactions with typical use
cases of the ubiquitous semantic Metaverse, such as remote education, work and
collaboration, entertainment and socialization, healthcare, and e-commerce
marketing. Furthermore, we outline the opportunities for constructing the
future ubiquitous semantic Metaverse, including scalability and
interoperability, privacy and security, performance measurement and
standardization, as well as ethical considerations and responsible AI.
Addressing those challenges is important for creating a robust, secure, and
ethically sound system environment that offers engaging immersive experiences
for the users and AR/VR applications.Comment: 18 pages, 7 figures, 3 table
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