AI and Big Data for Drug Discovery

Kavidopoulou, Aglaia; Syrigos, Konstantinos; Makrogkikas, Stylianos; Dlamini, Zodwa; Hull, Rodney; Marima, Rahaba; Skepu, Amanda; Koumoulos, Elias P.; Bakas, George; Vamvakaris, Ioannis; Evangelou, George; Lolas, Georgios

doi:10.1007/978-3-031-11199-0_7

Cited by 4 publications

(3 citation statements)

References 63 publications

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“…The history of AI in drug discovery can be traced back to the 1960s when the fi rst computer program, DENDRAL, was developed in Stanford university to assist in drug discovery [7,8]. Since then, AI has been used in various stages of drug discovery, including target identifi cation [9], lead optimization [10], and drug design [11].…”

Section: History Of Ai In Drug Discoverymentioning

confidence: 99%

Artificial Intelligence in Drug Discovery and Delivery: Advancements and Applications

Ali Mahjoub,

Sheikholislam

2023

J Biomed Res Environ Sci

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Artificial Intelligence (AI) has made significant progress in drug discovery and drug delivery and has become an active area of research. The use of AI in drug delivery has gained significant attention, with the development of new technologies and algorithms that enable more efficient drug delivery. The history of AI in drug discovery can be traced back to the 1960s, and since then, AI has been used in various stages of drug discovery, including target identification, lead optimization, and drug design. AI can aid in different stages of drug development, including drug discovery, formulation, and optimization. AlphaFold is an AI-powered deep learning system that uses neural networks to predict the structure of proteins to design drug molecules. The ultimate goal of using AI in pharmaceuticals is to minimize cost and time and achieve exceptional results without the need for laboratory presence. However, experimental validation is still necessary to verify the accuracy and reliability of the AI-generated results.

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Section: History Of Ai In Drug Discoverymentioning

confidence: 99%

Artificial Intelligence in Drug Discovery and Delivery: Advancements and Applications

Ali Mahjoub,

Sheikholislam

2023

J Biomed Res Environ Sci

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“…[34][35][36][37] Real-time monitoring Big data facilitates monitoring the state of patients with the use of medical equipment and the IoT and offers continuous real-time monitoring capabilities, which makes it feasible to begin therapy and intervention at an earlier stage. [26,38,39] Improving patient treatment…”

Section: Referencementioning

confidence: 99%

“…Drug development: Big data is used to identify potential drug targets and improve the efficiency of clinical trials [26].…”

mentioning

confidence: 99%

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

2023

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In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.

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Advanced AI Applications for Drug Discovery

Yingngam,

Sethabouppha

2024

Advances in Medical Technologies and Clinical Practice

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Addressing the critical challenge of lengthy and costly drug development, this chapter illuminates the transformative role of advanced artificial intelligence (AI) in drug discovery. It aims to dissect the impact of AI methodologies in streamlining these traditionally complex processes. This chapter begins by highlighting the inefficiencies of conventional drug discovery methods, emphasizing their resource-intensive nature. An in-depth discussion of how AI technologies are revolutionizing the identification of novel drug targets, optimizing the molecular structures of drug candidates, and accurately predicting drug efficacy and toxicity is needed. This exploration underscores AI's dual advantages: significantly reducing drug development timelines and expenses while simultaneously enhancing the precision of predictions, leading to safer and more effective drugs. This chapter concludes with a vision of a future where AI-driven methods are fully integrated with personalized medicine and genomics, signaling the onset of a new era in healthcare and therapeutic innovation.

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AI and Big Data for Drug Discovery

Cited by 4 publications

References 63 publications

Artificial Intelligence in Drug Discovery and Delivery: Advancements and Applications

Artificial Intelligence in Drug Discovery and Delivery: Advancements and Applications

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

Advanced AI Applications for Drug Discovery

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