Jai Woo Lee

Artificial intelligence is a disruptive area which transforms cutting-edge healthcare technology to analyze clinical workflows, sharpen diagnostics, and improve precision medicine. The goal of this review is to identify approaches involving a collaborative examination to determine the key features influencing the adoption of artificial intelligence methodologies in advanced cutting-edge solutions for metabolomics and drug design. In clinical and translational settings, a comprehensive investigation of legal and ethical principles will be included to highlight the significance of omics analysis and drug design in the application of artificial intelligence tools with artificial intelligence in healthcare, the real-world uses, and difficulties tied to societal and regulatory issues. The real-world effects of artificial intelligence for researchers and technicians can provide guidance for tailored strategies focused on leveraging potential to improve high-dimensional data analysis on metabolomics and drug design. As artificial intelligence methodologies continue to evolve, efforts must be directed toward structured frameworks that uphold human oversight and engagement to optimize the utility of artificial intelligence algorithms and big data methodologies. The key contributions of this study include a comprehensive overview of cutting-edge artificial intelligence methodologies and software programs in metabolomics and drug design, and critical perspectives which can solidify the future directions in the development of algorithmic approaches to bridge metabolomics and drug design.

The goals of this chapter are to review artificial intelligence and big data techniques in precision medicine and corroborate applications of artificial intelligence techniques to solve challenges in immunological disorders. The integration of artificial intelligence in precision medicine has strengthened immunology in biomedical sciences. Precision medicine detects the health outcomes of specific patients. Biomedical studies have shown that clinical research defining the integration of artificial intelligence and precision medicine will present solutions for the challenging issues in precision medicine. This chapter covers various features of artificial intelligence in precision medicine that may nourish biomedical science research. We discuss how artificial intelligence solidifies the technical environment of precision medicine and the application of artificial intelligence methods to analyze biomedical big data of immunological disorders.

Absenteeism at work, possibly leading to productivity loss in business, is related to various psychological, social, and economic factors. Since predicting absenteeism is involved with complex associations of such factors, appropriately utilizing machine learning algorithms is required in the analysis. Statistical pre-processing and applications of machine learning methods have developed the comprehensive analysis of massive social data for absenteeism. The aim of this study is to develop a quantitative approach to identify the associations of factors and classify the absenteeism by including the effect of factors in the high-dimensional data. This approach implements association analysis including odds ratio test and network analysis, and supervised learning with imbalanced classification with random forest, application of principal component analysis and penalized regression methods. The dataset in this study includes records of various types of absenteeism at workplace from July 2007 to July 2010 in Brazil. Our study shows that there exist strongly interacting factors and that specific factors are strongly associated with absenteeism. The proposed method is validated on publicly available data sets using random forest and penalized regression with k-fold cross validation in order to strengthen better generalizability. One of major findings in this study is to elucidate the associations of factors affecting absenteeism. Application to similarly structured social data improves the understanding of the complex interplay between social factors and absenteeism that are important for people analytics which can help organizations resolve management difficulties.

In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches. Key words Artificial intelligence Big Data Computer-aided drug design Statistics Mathematical modeling

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins’ 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug–target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein–protein interactions (PPI).