Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector

Pokhriyal, Prashant; Chavda, Vivek P.; Pathak, Manisha

doi:10.1002/9781119865728.ch17

Cited by 4 publications

(4 citation statements)

References 85 publications

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“…The integration of AI and multi-scale modeling as shown in Figure 1 is considered significant in pharmaceutics due to its potential to revolutionize drug discovery and development processes (DiNuzzo, 2022;Pokhriyal et al, 2023). AI offers a third-party perspective on manufacturing techniques, recommending improvements for optimal efficiency (Muqtadiroh et al, 2022).…”

Section: B Significance Of Integrating Ai and Multi-scale Modeling In...mentioning

confidence: 99%

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Chaudhari,

Puri

et al. 2024

Preprint

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This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

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Section: B Significance Of Integrating Ai and Multi-scale Modeling In...mentioning

confidence: 99%

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Chaudhari,

Puri

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…The integration of AI and multi-scale modeling is considered significant in pharmaceutics due to its potential to revolutionize drug discovery and development processes (DiNuzzo, 2022;Pokhriyal et al, 2023). AI offers a third-party perspective on manufacturing techniques, recommending improvements for optimal efficiency (Muqtadiroh et al, 2022).…”

Section: B Significance Of Integrating Ai and Multi-scale Modeling In...mentioning

confidence: 99%

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Chaudhari,

Puri

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…The AI-based image processing type of algorithm was used for these types of visual inspection and the issues associated with them. One of the other camera-based applications of AI was surface crack detection by using surface qualifies 7500, which is used to analyze hundreds of millions of data points per second with the help of graphical processing subunits [ 127 , 128 , 140 , 141 , 142 ]. Manufacturers may optimize product performance, decrease manufacturing hazards, and provide safe and effective parenteral and technologically advanced pharmaceutical products using AI data analysis, pattern recognition, and predictive modeling.…”

Section: Ai For Drug Deliverymentioning

confidence: 99%

“…The use of artificial intelligence in various segments of healthcare is growing daily, from the triage and screening of clinical risk prediction to diagnosis [ 141 , 240 ]. Clinical applications of AI have the potential to increase diagnosis accuracy and healthcare efficiency.…”

Section: Futuristic Overviewmentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

Self Cite

233

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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.

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Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector

Cited by 4 publications

References 85 publications

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

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