Authors :
Sai Sruthi Ganugu; Atishay Jain; Anjali Sharma; Onyeka Asumah
Volume/Issue :
Volume 8 - 2023, Issue 10 - October
Google Scholar :
https://tinyurl.com/2txrp3ej
Scribd :
https://tinyurl.com/mr3b7e6c
DOI :
https://doi.org/10.5281/zenodo.10087865
Abstract :
This research explores the transformative
impact of Artificial Intelligence (AI) and Machine
Learning (ML) in the pharmaceutical sector,
specifically focusing on drug discovery. Our objectives
are twofold: firstly, to evaluate the advantages,
limitations, and challenges posed by AI in drug
discovery; and secondly, to propose comprehensive
strategies for addressing these challenges. To meet these
objectives, we conducted a thorough review of existing
literature, emphasizing AI applications, notably deep
learning, within pharmaceutical research. We also
explored various aspects, such as Quantitative
Structure-Activity Relationship/Quantitative Structure-
Property Relationship (QSAR/QSPR) modeling, de
novo drug design, and chemical synthesis prediction.
Our approach involved case studies and large-scale
applications, extracting insights from diverse sources.
Our findings illustrate how AI can revolutionize drug
development, enhance drug design, and refine drug
screening. However, we acknowledge the persistent
challenges related to data availability and ethical
considerations, requiring careful attention to harness
AI's full potential in pharmaceutical research. Our
study underscores AI's growing impact on the
pharmaceutical industry, offering promising avenues
for increased research efficiency and potentially life-
saving discoveries. By addressing data and ethical
concerns, we believe that AI can pave the way for
groundbreaking advancements in pharmaceutical
research. This paper provides an in-depth overview of
AI's current state in pharmaceutical research and a
comprehensive framework for navigating this critical
domain.
Keywords :
Drug Discovery, Artificial Intelligence, QSAR/QSPR Modeling, Limitations, Pharmaceuticals, Machine Learning, Data Challenges, and Ethical Considerations.
This research explores the transformative
impact of Artificial Intelligence (AI) and Machine
Learning (ML) in the pharmaceutical sector,
specifically focusing on drug discovery. Our objectives
are twofold: firstly, to evaluate the advantages,
limitations, and challenges posed by AI in drug
discovery; and secondly, to propose comprehensive
strategies for addressing these challenges. To meet these
objectives, we conducted a thorough review of existing
literature, emphasizing AI applications, notably deep
learning, within pharmaceutical research. We also
explored various aspects, such as Quantitative
Structure-Activity Relationship/Quantitative Structure-
Property Relationship (QSAR/QSPR) modeling, de
novo drug design, and chemical synthesis prediction.
Our approach involved case studies and large-scale
applications, extracting insights from diverse sources.
Our findings illustrate how AI can revolutionize drug
development, enhance drug design, and refine drug
screening. However, we acknowledge the persistent
challenges related to data availability and ethical
considerations, requiring careful attention to harness
AI's full potential in pharmaceutical research. Our
study underscores AI's growing impact on the
pharmaceutical industry, offering promising avenues
for increased research efficiency and potentially life-
saving discoveries. By addressing data and ethical
concerns, we believe that AI can pave the way for
groundbreaking advancements in pharmaceutical
research. This paper provides an in-depth overview of
AI's current state in pharmaceutical research and a
comprehensive framework for navigating this critical
domain.
Keywords :
Drug Discovery, Artificial Intelligence, QSAR/QSPR Modeling, Limitations, Pharmaceuticals, Machine Learning, Data Challenges, and Ethical Considerations.
