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How AI Can Revolutionize the Drug Discovery Process

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AI can revolutionize healthcare's drug discovery process by accelerating the identification and optimization of drug candidates. A study by IBM and Oxford University researchers validated AI-generated antiviral drugs, potentially validated within months. Generative AI can design novel molecules targeting SARS-CoV-2 without needing prior virus 3D structure knowledge. This process, faster than the traditional 12-year drug development, could combat drug resistance and respond swiftly to viral threats. Yet, challenges lie ahead, including intellectual property rights, technological misuse, and drug safety. Beyond drug discovery, generative AI can enhance diagnosis, screening, personalised medicine, and overall healthcare innovation.

Artificial intelligence (AI) is a transformative technology with far-reaching implications. Its potential to revolutionize healthcare’s drug discovery process is unprecedented.

AI has shown promise in healthcare areas like medical imaging analysis, disease diagnosis, and personalized medicine. Its ability to process vast amounts of data and recognize complex patterns has made it a valuable tool for medical professionals.

However, AI’s most profound healthcare impact could be in drug discovery. AI’s computational power and predictive abilities can hasten the identification and optimization of drug candidates, transforming the complex, costly process.

The Role of AI in the Drug Discovery Process

In a groundbreaking study titled “Accelerating drug target inhibitor discovery with a deep generative foundation model,” researchers from IBM and Oxford University have validated a new class of AI-generated antiviral drugs. The research shows that AI-designed drugs can be synthesized and potentially validated within months. This could speed up medication delivery in future crises.

Published in Science Advances, the study demonstrates generative AI‘s potential to design novel molecules targeting SARS-CoV-2, the COVID-19 virus.

The team used an AI model, CogMol, trained on a dataset of molecules and proteins. Notably, CogMol generated viable antivirals without needing the virus’s 3D structure information


Through the CogMol model, the researchers generated 875,000 candidate molecules for two key protein targets of the virus: the spike protein (located on the outside of a coronavirus and is how the coronavirus enters human cells) and the main protease (an essential enzyme that plays a crucial role in the replication of the virus). These candidates underwent extensive predictive modelling and synthesis analysis, leading to four compounds per target selected for testing.

The synthesized compounds were then subjected to target inhibition and live virus neutralization tests,  revealing that two of the antivirals successfully targeted the main protease. Meanwhile, the other two targeted the spike protein and could neutralize all major COVID-19 variants.

The study’s results signify the immense potential of generative AI in accelerating the drug discovery process. AI’s ability to create new molecules allows scientists to combat drug resistance and quickly respond to evolving viral threats.

While more research and clinical trials are needed, this study paves the way for future AI-driven drug design and development, which could benefit global healthcare.

The Traditional Drug Discovery Process

As per the California Biomedical Research Association, it typically takes 12 years to develop a new drug from lab to patient. Throughout this process, approximately five out of 5,000 drugs that undergo preclinical testing (animal trials) make it to human testing. Eventually, only one out of these five drugs gets approved for human usage.

The cost of developing a new drug averages more than a billion dollars. This significant investment covers the extensive research, testing, and regulatory processes involved.

During the up to three-and-a-half-year preclinical research phase, scientists study diseases and their components and identify abnormalities in the body. They develop and test potential compounds in test tubes and living animals to evaluate their effects.

The clinical trial phases comprise three stages. Phase 1 trials, typically taking about a year, administer the drug to 20 to 80 healthy volunteers. Phase 2 trials, lasting around two years, test the drug’s effectiveness on 100 to 300 volunteers with the disease and establish dosages. Finally, Phase 3 trials involve 1,000 to 3,000 patients and take about three years, confirming the drug’s efficacy and safety.

Post successful clinical trials, a company must file a New Drug Application (NDA) with the FDA. Review can take up to six months. Upon approval, the drug becomes available for physicians to prescribe to patients. Post-approval, the pharmaceutical company must monitor and report any unknown side effects.

The drug discovery process involves years of research, testing, and regulatory steps, costing over a billion dollars. Therefore, the research on AI-generated antiviral drugs has significant implications for traditional drug development. It showcases the potential of generative AI in accelerating the process, potentially validating new drugs within months instead of years.

Harnessing the Potential of Generative AI in Healthcare

Generative AI has the potential to transform healthcare beyond the drug discovery process. By analyzing large datasets, it enhances diagnosis, screening, and personalized medicine, leading to earlier detection and more accurate treatments. It aids in increasing health plan enrollment through informative reminders.

Furthermore, generative AI interprets unstructured medical data, providing comprehensive insights. It enables predictive maintenance to predict equipment failure and optimize maintenance. Medical robots driven by AI assist in surgeries, while generative AI generates research ideas and answers questions.

These applications transform healthcare by improving patient care, cutting costs, and encouraging innovation.

Future Implications and Challenges Related to the AI Drug Discovery Process

AI-enabled drug discovery holds immense potential but also presents significant challenges. On the positive side, AI can revolutionize the speed and economics of the industry, accelerating target identification, molecular simulations, and drug design. It offers the possibility of generating novel drug molecules and prioritizing candidates more efficiently.

However, challenges lie ahead, including unresolved issues related to intellectual property rights, technological misuse, and ensuring drug safety and efficacy. Lawyers and policymakers must prepare for these challenges to maximize the benefits and navigate the ethical and regulatory implications of the AI drug discovery process.


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Maria Webb
Technology Journalist
Maria Webb
Technology Journalist

Maria is a technology journalist with over five years of experience with a deep interest in AI and machine learning. She excels in data-driven journalism, making complex topics both accessible and engaging for her audience. Her work is prominently featured on Techopedia, Business2Community, and Eurostat, where she provides creative technical writing. She holds a Bachelor of Arts Honours in English and a Master of Science in Strategic Management and Digital Marketing from the University of Malta. Maria's background includes journalism for, covering a range of topics from local events to international tech trends.