Kokudeva M, Vichev M, Naseva E, Miteva DG, Velikova T. Artificial intelligence as a tool in drug discovery and development. World J Exp Med 2024; 14(3): 96042 [PMID: 39312699 DOI: 10.5493/wjem.v14.i3.96042]
Corresponding Author of This Article
Maria Kokudeva, PharmD, PhD, Research Assistant, Department of Pharmacology and Toxicology, Faculty of Pharmacy, Medical University of Sofia, ul. Dunav 2, Sofia 1000, Bulgaria. kokudeva.mariya@gmail.com
Research Domain of This Article
Research & Experimental Medicine
Article-Type of This Article
Opinion Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Maria Kokudeva, Department of Pharmacology and Toxicology, Faculty of Pharmacy, Medical University of Sofia, Sofia 1000, Bulgaria
Mincho Vichev, Healthcare Solutions, Sofia 1404, Bulgaria
Emilia Naseva, Faculty of Public Health, Medical University of Sofia, Sofia 1431, Bulgaria
Dimitrina Georgieva Miteva, Department of Genetics, Faculty of Biology, Sofia University St. Kliment Ohridski, Sofia 1164, Bulgaria
Dimitrina Georgieva Miteva, Tsvetelina Velikova, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
Co-first authors: Maria Kokudeva and Mincho Vichev.
Author contributions: Kokudeva M and Vichev M were involved equally in conceptualizing the idea and writing the draft; Miteva D, Naseva E and Velikova T wrote additional sections in the paper; Vichev M was responsible for the critical revision of the manuscript for relevant intellectual content; Velikova T was responsible for project administration and funding acquisition; All authors approved the final version of the paper prior to submission.
Supported bythe European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, No. BG-RRP-2.004-0008.
Conflict-of-interest statement: All authors declare they have no conflicts of interest to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Maria Kokudeva, PharmD, PhD, Research Assistant, Department of Pharmacology and Toxicology, Faculty of Pharmacy, Medical University of Sofia, ul. Dunav 2, Sofia 1000, Bulgaria. kokudeva.mariya@gmail.com
Received: April 25, 2024 Revised: August 6, 2024 Accepted: August 12, 2024 Published online: September 20, 2024 Processing time: 125 Days and 21.4 Hours
Abstract
The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.
Core Tip: Embracing artificial intelligence (AI) expedites drug discovery and development by streamlining computational chemistry, molecular modeling, and data mining. Leveraging AI-driven algorithms enhances the accuracy and efficiency of identifying potential drug candidates and predicting their pharmacological properties. Integrating machine learning and deep learning frameworks into pharmaceutical research optimizes decision-making, accelerates drug design cycles, and ultimately advances novel therapies for various diseases.
