Artificial Intelligence and Machine Learning Applications in Vaccine Development

Sarmadi, Ali; Hassanzadeganroudsari, Majid; Soltani, M.

doi:10.1002/9781119865728.ch11

Cited by 7 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the models may not be able to accurately predict drug responses for individuals who deviate significantly from the average response. This is particularly concerning for drugs that have a wide range of responses in different patients (such as in cancer), where the variability can be significant [ 220 ].…”

Section: Limitations Of Ai Toolsmentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

View full text Add to dashboard Cite

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

show abstract

Section: Limitations Of Ai Toolsmentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

View full text Add to dashboard Cite

show abstract

“…This issue is particularly noteworthy for drugs exhibiting diverse responses in various patients, where substantial variability is a key factor. 76…”

Section: Constrained Capability In Addressing Variabilitymentioning

confidence: 99%

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

Bhabad,

Lamkhade,

Koyate

et al. 2023

IJCAAP

View full text Add to dashboard Cite

Artificial intelligence (AI) represents a specialized field within computer science, empowering machines to efficiently process and analyze intricate data. The surge in AI-focused research has notably accelerated, particularly in its growing role within healthcare services and research. This review delves into the manifold opportunities and challenges that AI presents in the realms of healthcare and pharmaceutical research. To compile relevant literature, a comprehensive search was conducted across databases such as PubMed, Science Direct, and Google Scholar. The article extensively explores the application of AI in diverse healthcare aspects such as disease diagnosis, digital therapy, personalized treatment, drug discovery, and the anticipation of epidemics or pandemics. The predominant AI technologies employed include deep learning and neural networks. The review emphasizes the pivotal role of advancing AI technologies in fostering rapid and cost-effective healthcare and pharmaceutical research. The anticipated outcome is an improved delivery of services to the general public, reflecting the transformative impact of AI on scientific endeavors.

show abstract