Drug discovery is associated with high costs, lengthy timelines, and significant failure rates. The emergence of artificial intelligence (AI), machine learning (ML), and deep learning technologies are transforming this landscape by harnessing extensive datasets and computational power to accelerate drug discovery, enhance precision, and improve efficiency.
AI in protein structure prediction
Determination and characterization of protein structures is a pivotal step during the target identification stage of drug discovery that is renowned for being slow, expensive, and often yielding low-resolution or poor-quality results. AI-driven technologies like AlphaFold and RoseTTAFold are transforming the way scientists approach protein structure prediction and gained recognition as Science’s breakthrough of the year in 2021 (1). AlphaFold's role was significant in understanding the structure of the SARS-CoV-2 spike protein, aiding in the rapid development of COVID-19 vaccines (2).
By understanding the 3D structure of proteins, researchers can identify potential drug targets more efficiently. This capability is particularly powerful in the case of complex diseases where the target structures are not well understood, as well as for expanding the horizons of "druggable" targets.
AI in molecular modeling and clinical prediction
In drug design, AI algorithms can be used to streamline and enhance molecular modeling. Algorithms are able to screen vast compound libraries and predict molecular behavior with high precision (3).
AI's capacity for accurately and efficiently analyzing large genomic datasets is pivotal in personalized medicine. By understanding individual genetic profiles, AI facilitates the development of tailored treatments by enhancing their efficacy and minimizing adverse reactions (4). This approach is particularly beneficial in diseases where genetic variations play a significant role in disease progression and treatment response.
Optimizing clinical trial design with AI
AI-driven solutions can reduce time and cost burdens by optimizing patient selection and refining endpoints during trial design (5). AI algorithms can predict optimal patient cohorts which enhances the effectiveness of trials and increases the likelihood of successful outcomes (6).
Throughout the trial process, AI tools can monitor and analyze trial data in real-time. This capability allows quicker, data-driven adjustments during trials, improving their efficiency and success rate (7). AI also plays a crucial role in safety data monitoring, creating comprehensive risk profiles, and documenting safety measures efficiently (8). However, the use of AI in clinical trials and healthcare raises important data privacy concerns: to safeguard patient information, it is essential to establish and strictly follow regulatory guidelines (9).
Conclusions and future perspectives
From revolutionizing protein structure prediction to optimizing clinical trials, AI's contributions are multifaceted and invaluable. As the field continues to evolve, it is crucial to address ethical and data privacy concerns to ensure responsible and beneficial use of AI in healthcare.
If you're interested in staying on top of the latest trends transforming drug discovery, you won’t want to miss our latest whitepaper. We expand our discussion of the revolution of AI in drug discovery and delve into more emerging trends in the field.
References
- Science’s 2021 Breakthrough of the Year: AI brings protein structures to all. www.science.org. https://www.science.org/content/article/breakthrough-2021
- Computational predictions of protein structures associated with COVID-19. www.deepmind.com. https://www.deepmind.com/open-source/computational-predictions-of-protein-structures-associated-with-covid-19
- Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2020;26(1). doi:10.1016/j.drudis.2020.10.010
- Ghaffar Nia N, Kaplanoglu E, Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence. 2023;3(1). doi:10.1007/s44163-023-00049-5
- Cascini F, Beccia F, Causio FA, Melnyk A, Zaino A, Ricciardi W. Scoping review of the current landscape of AI-based applications in clinical trials. Frontiers in Public Health. 2022;10. doi:10.3389/fpubh.2022.949377
- Umscheid CA, Margolis DJ, Grossman CE. Key Concepts of Clinical Trials: A Narrative Review. Postgraduate Medicine. 2011;123(5):194-204. doi:10.3810/pgm.2011.09.2475
- Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemporary Clinical Trials Communications. 2018;11:156-164. doi:10.1016/j.conctc.2018.08.001
- Askin S, Burkhalter D, Calado G, Samar El Dakrouni. Artificial Intelligence Applied to clinical trials: opportunities and challenges. Artificial Intelligence Applied to clinical trials: opportunities and challenges. 2023;13(2). doi:10.1007/s12553-023-00738-2
- Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics. 2021;22(1). doi:10.1186/s12910-021-00687-3