AI in Biomedical Research Is Revolutionizing Drug Development, Clinical Innovation


AI is expected to significantly quicken the pace of drug design and development, while improving the success rate of new medicines.

Artificial intelligence (AI) is on the verge of transforming how physicians and scientists conduct biomedical research. From hunting for new treatments to running clinical trials, AI is starting to reshape every step of the scientific process. Some experts think we're headed for a world where AI will take over and replace researchers, but the reality will likely be more human-focused than that.

AI is a tool—a powerful one, but still just a tool. It needs human guidance to function optimally and ethically. However, it remains valuable to examine how exactly researchers are beginning to use AI as their lab partner.

AI in Drug Design and Development

AI is the future of drug design and development. Many experts predict AI will significantly quicken the pace and improve the success rate of developing new medicines through automation, prediction, and insight generation. But it will complement human discovery skills, not replace them.

Many experts predict AI will significantly quicken the pace and improve the success rate of developing new medicines through automation, prediction, and insight generation. Image Credit: © Toowongsa -

Many experts predict AI will significantly quicken the pace and improve the success rate of developing new medicines through automation, prediction, and insight generation. Image Credit: © Toowongsa -

Here are some key ways that AI may impact drug design and development in the coming years:

  • Virtual screening and lead generation. AI can rapidly screen millions of compounds to identify promising drug candidates by predicting activity and properties. This can significantly accelerate the early stages of drug discovery.1
  • Optimization of drug candidates. Once leads are identified, AI can help optimize their chemical structures to improve potency, selectivity, and other drug-like properties. AI models can also propose structural changes and predict their effects.2
  • Analysis and interpretation of data. The drug development process generates vast amounts of complex data. AI techniques like machine learning can uncover patterns and insights from the data that may be difficult for humans to discern.3
  • Predicting clinical trial outcomes. AI algorithms can analyze data from past trials to predict the outcomes of new clinical trials better. This can help improve trial design and recruitment.4
  • Accelerating regulatory review. AI can mine existing regulatory documentation to generate summaries, improving review efficiency. It may also predict potential approval outcomes.5

From the above, it’s easy to think of how AI can improve the overall drug development process. Experts believe AI could help cut the average drug development timelines by up to 4 years. Ultimately, AI will bring greater productivity, predictive power, and insight-generation capabilities to bear on the complex, multidisciplinary process of drug research and development.

Impact of AI on Biomarker Development

Biomarker development is another aspect of drug development that AI will improve, as it can uncover biomarkers from diverse datasets that can improve clinical trials and personalized medicine approaches, allowing us to find effective treatments for people with certain genetic makeups and rare conditions.

AI will also help patient outcomes by assisting scientists in predicting toxicity, optimizing clinical trials, and continuously monitoring patient data post-treatment.

AI in Clinical Research and Drug Discovery

Although we can expect AI to become an indispensable assistant, we do not expect AI to become autonomous or a complete replacement for human skills and oversight in the highly complex clinical research process. Instead, close collaboration between humans and AI will drive the field forward.

For example, AI will take on more roles in trial design, data analysis, patient monitoring, drug discovery, and regulatory review. However, human oversight over ethics, goals, context, and decisions will remain critical. AI will become highly capable of finding patterns in data, predicting outcomes, and optimizing protocols, but human expertise to interpret insights will still be essential. AI may help optimize patient monitoring via apps and wearables, but human involvement in oversight and interaction will still be needed.

AI will be very productive in drug discovery at specific tasks, but human creativity and scientific insight will still be irreplaceable. AI may handle data processing and forms for regulatory review, but human assessment of risks, benefits, and approval decisions will stay crucial.

One thing's for sure—the future of medical research will be a team effort between innovative scientists, physicians, and intelligent machines. We should explore how we can collaborate to push the boundaries of health care and help people live optimally healthy and productive lives. AI isn't going to put physicians and scientists out of jobs—it will help them do their jobs more efficiently and do more good for patients’ health and lives than ever before.

About the Author

Kennedy Schaal is CEO and senior biologist at Rejuve.BIO.


  1. Schneider G. Generative Models for Artificially-intelligent Molecular Design. Mol Inform. 2018;37(1).doi:10.1002/minf.201880131
  2. Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-Knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res Rev. 2019;49:49-66. doi:10.1016/j.arr.2018.11.003
  3. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773-780. doi:10.1016/j.drudis.2018.11.014
  4. Wainberg M, Merico D, Delong A, et al. Deep learning in biomedicine. Nat Biotechnol. 2018;36:829-838. doi:10.1038/nbt.4233
  5. Soomro TA, Zheng L, Afifi AJ, Ali A, Yin M, Gao J. Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research. Artif Intell Rev. 2022;55(2):1409-1439. doi:10.1007/s10462-021-09985-z
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