Disruption has arrived in the pharmaceutical and biotech industry. Driven by artificial intelligence (AI) and machine learning (ML) technologies, new possibilities include everything from molecular design to predictive patient reaction models. However, despite a clear connection between the science of drug discovery, ML, and business decision making, there is a disconnect between the tools that exist and the specialists utilizing them. It’s only by bridging this gap that the full potential of this technology will be realized. In the Artificial Intelligence in Pharma and Biotech online short course from MIT Sloan School of Management, you’ll discover the benefits and challenges of AI tools within this sector. Over six weeks, gain insight into the current state of technology in the industry and explore ways that it can be applied to the drug discovery and distribution processes. You’ll learn how AI can be utilized in biological and generative modeling, and examine the impact of ML on the design and management of clinical trials. With insights into the relevance, practical implications, and business impact of these technologies, you’ll be able to position yourself ahead of the curve as innovation reshapes the industry.
Over the course of six weeks, dive into the existing and potential applications of AI and ML in the pharmaceutical and biotech industry. Guided by expert MIT faculty, you’ll gain insight into the optimal AI tools for this industry and explore how they can be leveraged for early drug discovery. Unpack AI’s potential to help promote research efforts into biology and diseases on a cellular level, and how it can assist with tasks like biomarker identification and disease tracking. Finally, you’ll investigate the impact of new AI modalities on patient stratification, and assess the limitations and promises of using ML in the design and management of clinical trials. You’ll walk away from the program with an understanding of AI’s broader business implications for the pharma and biotech industry.
The Landscape of Artificial Intelligence (AI) in the Pharmaceutical Industry
Using AI for Early Drug Discovery: From Small Molecules to Biologics
Modeling the Biological Underpinning of Disease
Biomarkers, Discovery, and Patient Stratification
Design and Management of Clinical Trials
Business and Innovation in Pharma
This program is designed for business leaders in pharmaceutical science and other scientific fields who want to understand how AI can be integrated into their organization. The program is ideal for professionals who are interested in the various AI and ML tools available, and want to learn how to apply them in their research and work. Researchers, specialists, data scientists, software developers and analysts working for a pharmaceutical company will also benefit from the course as they learn the broader business implications of AI applications in pharma and biotech, and how these technologies can be introduced within their context.
This course is delivered in our Self-Paced Online format which enables you to participate at your own pace within weekly modules. This course runs over 6 weeks with an estimated 6-8 hours per week of study time
Earn a certificate of course completion from the MIT Sloan School of Management
You will learn through a variety of formats including interactive videos, practice quizzes, presentations, assignments, and discussion forums
You will have access to a Success Adviser who will help you manage your time, and support you with any administrative or technical queries you might have
"Initially I found the course potentially useful for understanding the current state of knowledge in this field and for assessing the expertise of the speakers. However, I was concerned by indications that the recorded material may be significantly outdated, which has direct impact on the whole course content and quality. Three examples: - Najat Khan left Johnson Johnson in May 2024, yet the video credits identify her with that affiliation as though it were current (MIT APH M5U2 Casebook Video 1, 0:15 aprox.), this suggests that the recording is at least two years old. - Dr. Mathai Mammen is described in the module text as a former Johnson Johnson executive. However, in MIT APH M6U1 Casebook Video 6, at approx. (0:18), he refers to himself as currently working there. As he left Johnson Johnson in February 2023, this suggests that the recording is approximately three years old. - James Bradner left Novartis in December 2023 and subsequently joined Amgen. Yet both the course materials and video identify him as a current Novartis employee. This suggests that his video may be at least two and a half years old. Given the speed of AI development, adoption, and regulatory evolutionparticularly in pharmaceutical research, development, and manufacturingthis raises concerns about whether the course content adequately reflects the current state of the field. Considering the substantial course fee, participants can reasonably expect content that is regularly reviewed, updated, and transparently dated. I would therefore suggest: - Updating the course content and speaker affiliations regularly, and clearly displaying the recording date for each video. - Including a greater proportion of speakers from the pharmaceutical industry who have direct, current operational experience in drug development, manufacturing, quality, regulatory affairs, and implementation of AI-enabled systems. - Improving speaker selection and production standards to ensure clear enunciation and intelligible delivery. Some presentations are difficult to follow. - Reviewing the presentation quality of all videos, including visual professionalism and suitability for an academic course environment. I also found Regina Barzilays on-camera presentation somewhat distracting, as her hairstyle frequently obscured much of her face and eyes. A more professional visual setup would improve the viewing experience."
Tatiana M
"Its a perfect balance for the AI course for non-computer people and fantastic for especially for bio people."
Debalina S
"Artificial Intelligence in Pharma and Biotech is an essential program for technical leaders. It provides a rigorous, data-driven framework for navigating the intersection of machine learning and drug discovery. The curriculum successfully bridges the gap between algorithmic potential and practical, large-scale implementation in the life sciences."
Eli Seunghun L
"I found this course to be informative and insightful in understanding the intersection and overlap between emerging AI technologies and the pharmaceutical and biotechnology space. The lecture videos were informative, and course support ensured questions were answered. I felt the weekly assignments were a good way to reinforce concepts learned that week, and helped drive deeper understanding of the topic by posing real-life scenarios to which the skills could be applied"
Aamir R
"This course content and the professors were outstanding. The invited lecturers were also top of thier game. The course is challenging and dense within 6 weeks, but as this was an MIT course , it was expected. Well worth, the time, effort and expense."
Laurie M
"Absolutley amazing course. It went above and beyond any of my expectations. Great learning experience. MIT at its best!"
Steven K
"Excellent course that provides an overview of AI applications in the Pharmaceutical and Biotech Industries. This is a must for forming and understanding how to align company strategy with AI in the field."
Yngve M
"Excellent AI course for people in the industry new to ML or those ones wanting a refresher. Content is highly relevant and set up is case driven, what makes it very efficient. Some parts of course may need updating as ML developments are accelerating. Course speed is moderate to high and every week is finalized with a graded assingment. Faculty varies per topic, unfortunately some communicate less effectively on-line. Dont expect any interaction with faculty. Group of participants was great, great diversity in role and background. Plenty opportunity for discussion and on-line networking. In all, value for money course!"
Barry D
"I'm excited to share that Ive successfully completed the MIT Professional Education course: Artificial Intelligence in Drug Discovery and Development - a powerful learning experience at the intersection of AI and life sciences. Over the past few weeks, Ive had the opportunity to explore how machine learning, deep learning, and data science are revolutionizing drug discovery, clinical trials, and precision medicine. The course, led by world-class MIT faculty and industry experts, offered deep insights into: AI-driven molecular design Predictive modeling for clinical trial outcomes Biomarker discovery and omics integration Real-world applications and ethicalregulatory considerations What made this course truly valuable was its practical, real-world approach. The program didnt just teach theory - it showcased how AI is actually being applied by pharma and biotech companies to accelerate innovation and improve patient outcomes."
Bhavana A
"Artificial Intelligence in Pharma and Biotech is one of the best courses from MIT. Each Module gives an in depth understanding of AI. I am amazed by Professor Barzilay's work and the way COIVID situation was handled! I strongly recommend this course to any one who is interested to know about what happened in the past and what is way forward for Artificial Intelligence in PharmaBiotech industries."
Rajani M
Same topic, Similar duration - Broader exploration across all institutes
Speak with an Advisor
Prashansa Uttam
Programme Advisor
Executive Education Office
Fill out the form below and our team will get back to you within 24 hours.
