scholarly journals Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

2021 ◽  
Author(s):  
Kartikay Prasad ◽  
Vijay Kumar
Keyword(s):  
Artificial Intelligence ◽  
Drug Development ◽  
Structural Biology ◽  
Drug Repurposing ◽  
Current Status ◽  
Full Potential ◽  
Potential Applications ◽  
Infectious Proteins ◽  
Applications Of Ai

Abstract It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing and development and motivate researchers in harnessing AI potentials in the fight against COVID-19.

scholarly journals A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

2022 ◽  
Vol 54 (8) ◽  
pp. 1-32
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Zhaolei Zhang ◽  
Keqin Li ◽  
Philip S. Yu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Development ◽  
Medical Image ◽  
Vaccine Development ◽  
Scientific Research ◽  
Image Inspection ◽  
Potential Applications ◽  
Detection And Diagnosis ◽  
Applications Of Ai

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

A review on the role of artificial intelligence in stem cell therapy: An initiative for modern medicines

2020 ◽  
Vol 21 ◽  
Author(s):  
Pravin Shende ◽  
Nikita P. Devlekar
Keyword(s):  
Artificial Intelligence ◽  
Individual Cell ◽  
Home Automation ◽  
Science And Engineering ◽  
Prediction Of Mortality ◽  
Wide Range ◽  
Potential Applications ◽  
Applications Of Ai

: Stem cells (SCs) show a wide range of applications in the treatment of numerous diseases including neurodegenerative diseases, diabetes, cardiovascular diseases, cancer, etc. SC related research has gained popularity owing to the unique characteristics of self-renewal and differentiation. Artificial intelligence (AI), an emerging field of computer science and engineering has shown potential applications in different fields like robotics, agriculture, home automation, healthcare, banking, and transportation since its invention. This review aims to describe the various applications of AI in SC biology including understanding the behavior of SCs, recognizing individual cell type before undergoing differentiation, characterization of SCs using mathematical models and prediction of mortality risk associated with SC transplantation. This review emphasizes the role of neural networks in SC biology and further elucidates the concepts of machine learning and deep learning and their applications in SC research.

Artificial Intelligence and Cancer Drug Development

2021 ◽  
Vol 16 ◽  
Author(s):  
Fan Yang ◽  
Jerry D. Darsey ◽  
Anindya Ghosh ◽  
Hong-Yu Li ◽  
Mary Q. Yang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Development ◽  
De Novo ◽  
Drug Repositioning ◽  
De Novo Design ◽  
Cancer Drug ◽  
Target Validation ◽  
Cancer Drugs ◽  
Research Drug ◽  
Applications Of Ai

Background: The development of cancer drugs is among the most focused “bench to bedside activities” to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and AI. In the meantime, challenges, like curating the data of low quality, remain to be resolved. Objective: This review focused on the recent advancements in and challenges of AI in developing cancer drugs. Method: We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies. Results and Conclusion: AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

scholarly journals Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1575
Author(s):  
Silvia Pecere ◽  
Sebastian Manuel Milluzzo ◽  
Gianluca Esposito ◽  
Emanuele Dilaghi ◽  
Andrea Telese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care Professionals ◽  
Gastrointestinal Diseases ◽  
Controlled Trials ◽  
Clinical Settings ◽  
High Quality ◽  
Potential Applications ◽  
Malignant Lesions ◽  
Applications Of Ai

The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.

scholarly journals NRF2 drug repurposing using a question-answer artificial intelligence system

2019 ◽  
Author(s):  
Michel-Edwar Mickael ◽  
Marta Pajares ◽  
Ioana Enache ◽  
Gina Manda ◽  
Antonio Cuadrado
Keyword(s):  
Artificial Intelligence ◽  
Drug Repurposing ◽  
Computational Method ◽  
Lipoxygenase Inhibitor ◽  
Nrf2 Pathway ◽  
Intelligence System ◽  
Drug Compounds ◽  
Potential Applications ◽  
Inhibitor Drug

AbstractDrug repurposing represents an innovative approach to reduce the drug development timeline. Text mining using artificial intelligence methods offers great potential in the context of drug repurposing. Here, we present a question-answer artificial intelligence (QAAI) system that is capable of repurposing drug compounds. Our system employs a Google semantic AI universal encoder to compute the sentence embedding of an imposed text question in relation to publications stored in our RedBrain JSON database. Sentences similarity is calculated using a sorting function to identify drug compounds. We demonstrate our system’s ability to predict new indications for already existing drugs. Activation of the NRF2 pathway seems critical for enhancing several diseases prognosis. We experimentally validated the prediction for the lipoxygenase inhibitor drug zileuton as a modulator of the NRF2 pathway in vitro, with potential applications to reduce macrophage M1 phenotype and ROS production. This novel computational method provides a new approach to reposition of known drugs in order to treat neurodegenerative diseases. Github for the database and the code can be downloaded fromhttps://gist.github.com/micheledw/5a165b44345d45105d715340b88c756b

scholarly journals Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

2020 ◽  
Vol 5 (3) ◽  
pp. 247-285
Author(s):  
M. A. Kraus ◽  
M. Drass
Keyword(s):  
Artificial Intelligence ◽  
Structural Engineering ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Current Status ◽  
Future Research ◽  
Structural Glass ◽  
Material Parameter Identification ◽  
Cut Edge ◽  
Potential Applications

Abstract’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.

scholarly journals Artificial Intelligence in Education: A Panoramic Review

2020 ◽  
Author(s):  
Kashif Ahmad ◽  
Junaid Qadir ◽  
Ala Al-Fuqaha ◽  
Waleed Iqbal ◽  
Ammar El-Hassan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Research Work ◽  
Educational Data Mining ◽  
Research Trends ◽  
Drop Out ◽  
Future Research ◽  
Artificial Intelligence In Education ◽  
Potential Applications ◽  
Market Opportunities ◽  
Applications Of Ai

Motivated by the importance of education in an individual's and a society's development, researchers have been exploring the use of Artificial Intelligence (AI) in the domain and have come up with myriad potential applications. This paper pays particular attention to this issue by highlighting the future scope and market opportunities for AI in education, the existing tools and applications deployed in several applications of AI in education, research trends, current limitations and pitfalls of AI in education. In particular, the paper reviews the various applications of AI in education including student grading and evaluations, students' retention and drop out prediction, sentiment analysis, intelligent tutoring, classrooms' monitoring and recommendation systems. The paper also provides a detailed bibliometric analysis to highlight the research trends in the domain over six years (2014--2019). For this study, we analyze research publications in various related sub-domains such as learning analytics, educational data mining (EDM), and big data in education. The paper analyzes educational applications from different perspectives. On the one hand, it provides a detailed description of the tools and platforms developed as the outcome of the research work achieved in these applications. On the other side, it identifies the potential challenges, current limitations and hints for further improvement. We also provide important insights into the use and pitfalls of AI in education. We believe such rigorous analysis will provide a baseline for future research in the domain.

scholarly journals Challenges in Chagas Disease Drug Development

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2799 ◽  
Author(s):  
Amanda F. Francisco ◽  
Shiromani Jayawardhana ◽  
Francisco Olmo ◽  
Michael D. Lewis ◽  
Shane R. Wilkinson ◽  
...  
Keyword(s):  
Drug Development ◽  
Chagas Disease ◽  
High Throughput Screening ◽  
Public Health Problem ◽  
Drug Repurposing ◽  
Current Status ◽  
Laboratory Research ◽  
Important Public Health Problem ◽  
Treatment Regimens ◽  
Compound Libraries

The protozoan parasite Trypanosoma cruzi causes Chagas disease, an important public health problem throughout Latin America. Current therapeutic options are characterised by limited efficacy, long treatment regimens and frequent toxic side-effects. Advances in this area have been compromised by gaps in our knowledge of disease pathogenesis, parasite biology and drug activity. Nevertheless, several factors have come together to create a more optimistic scenario. Drug-based research has become more systematic, with increased collaborations between the academic and commercial sectors, often within the framework of not-for-profit consortia. High-throughput screening of compound libraries is being widely applied, and new technical advances are helping to streamline the drug development pipeline. In addition, drug repurposing and optimisation of current treatment regimens, informed by laboratory research, are providing a basis for new clinical trials. Here, we will provide an overview of the current status of Chagas disease drug development, highlight those areas where progress can be expected, and describe how fundamental research is helping to underpin the process.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

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