By Gagan Kumar
The convergence of open source technology and artificial intelligence is transforming drug discovery, introducing new standards of transparency, collaboration, and innovation. On October 30th, leaders from research, industry, and academia gathered at the AI for Drug Discovery Forum to explore how these technologies can address healthcare’s most complex challenges and launch a new initiative to drive their development and adoption. (Find opportunities to participate at the end of this blog.)
Sponsored by Red Hat, IBM Research, the Mass Open Cloud (MOC), Boston University’s Hariri Institute, and other research institutions, this event highlighted the power of open source AI in drug discovery and introduced a new AI Alliance working group focused on drug discovery using new open source AI models. Participants in the event came from software development, pharmaceutical, medical research, data science, government, and other fields, reflecting the importance of collaboration across multiple domains to successfully leverage AI for healthcare.
Why open source matters for AI in drug discovery
Drug discovery is a complex, resource-intensive process often requiring years and substantial investment. AI promises to streamline this process, enhancing precision and accelerating time-to-discovery. However, for AI to fully realize its potential, especially in healthcare, transparency, accessibility, and collaboration are essential. Open source AI frameworks allow researchers to access shared models, code, and data, fostering innovation and reproducibility that cannot be matched by proprietary solutions.
Red Hat’s commitment to open source development enables this approach. Through contributions to open AI initiatives, Red Hat supports healthcare research by providing open, scalable solutions that empower researchers globally. The long-running partnership between Red Hat Research and the Mass Open Cloud, a community-driven, open-access cloud platform, is also foundational to creating the open source ecosystem needed to achieve these goals. The MOC fosters deep collaboration between universities and industry, bridging the gap between academic research and practical AI application. Unlike proprietary clouds, MOC operates as a shared platform, designed to facilitate open research, accessible infrastructure, and a governance model that supports equitable resource use.
With Red Hat OpenShift AI deployments, the MOC enables researchers to leverage scalable, flexible infrastructure, addressing the unique demands of complex drug discovery research. MOC’s commitment to transparency and accessibility ensures that research outputs are openly documented and accessible, building a global, collaborative research community.
Red Hat OpenShift AI and the Mass Open Cloud in action
The Commonwealth of Massachusetts is betting on open source initiatives and practices to make AI for drug discovery a reality. That was the message from Yvonne Hao, Secretary of the Executive Office of Economic Development, in opening remarks to the forum. Keynote speaker Joseph Loscalzo, MD, PhD, Physician-in-Chief Emeritus, Brigham and Women’s Hospital, spoke about challenges in contemporary drug discovery and how open sharing of experimental procedures among researchers will expedite the process of drug discovery and investigation.
One highlight of the forum was the hands-on session where participants engaged with pre-deployed models created by IBM researchers, utilizing Red Hat OpenShift’s standard platform on the Mass Open Cloud. During this session, participants used notebooks within RHOAI workbenches, enabling them to test and interact with the models in a streamlined environment. This session demonstrated how Red Hat OpenShift AI enables researchers to build, test, and scale models rapidly, allowing researchers to focus on scientific challenges rather than operational barriers. Participants used their MOC access at the event to run inference, use notebooks to submit various types of protein combinations to the new open source models, and learn more about how researchers create and train open source models.
Anyone with an MOC account can join the Drug Discovery project to explore and experiment with the same models featured in the hands-on demo.
Red Hat Research Senior Technical Product Manager Gagan Kumar helped to coordinate the demo team, and Red Hat Research Senior Principal Software Engineer Jason Schlessman worked with the drug discovery research to get their models (such as MAMMAL) and associated notebooks running on the public open source MOC environment. Using Red Hat OpenShift AI coupled with early integration on the MOC, Red Hatters like Jason made it easy for researchers to deploy, scale resources, and experiment with data on various models during the forum and maintain access to the project after the event.
Together, Red Hat OpenShift AI and MOC deliver a powerful ecosystem for innovation, reducing infrastructure burdens and accelerating research breakthroughs. Anyone with an MOC account can join the Drug Discovery project to explore and experiment with the same models featured in the hands-on demo. This open-access setup allows users to engage directly with the models and tools used during the forum, providing a valuable opportunity to further investigate AI-driven drug discovery techniques on the Mass Open Cloud.
Launching a new working group for AI-driven drug discovery
The event also celebrated the creation of an AI Alliance working group dedicated to advancing drug discovery through AI. This new working group will unite experts across fields and institutions, including interim co-lead Heidi Dempsey, US Research Director for Red Hat Research, focusing on open source tools, ethical frameworks, and reproducible models specifically tailored for drug discovery.
The working group’s core objectives are to:
- Foster interdisciplinary collaboration: Enabling biologists, chemists, and data scientists to contribute to AI model development
- Promote reproducibility in AI research: Ensuring model accuracy, accessibility, and transparency
- Develop an open source repository of models and data: Creating a valuable resource for researchers worldwide
This open and collaborative model supports ethical AI development in drug discovery, ensuring AI solutions remain accessible, transparent, and scientifically robust.
Real-world impact and future prospects
The AI for Drug Discovery Forum illustrated how AI models can streamline the analysis of large datasets, predict molecular interactions, and optimize drug candidates, advancing tasks that traditionally took months or years. The forum’s hands-on session demonstrated how Red Hat OpenShift AI and MOC effectively support these high-demand computational workloads, making it possible to achieve faster, more precise research outcomes. By using the MOC, attendees were able to immediately work with new open source models from IBM integrated with the Red Hat OpenShift AI platform, rather than having to download, install, and run many different pieces of software on their own. The MOC’s high-performance CPUs and GPUs also accelerated processing for workflows that used these models.
Red Hat OpenShift’s standard platform on the Mass Open Cloud. Photo by ©Kelly Davidson Studio
The event discussions also emphasized the importance of accessible data and ethical considerations in AI development. By operating on an open source infrastructure like the MOC, researchers can enhance transparency in their workflows while ensuring data security and ethical handling of sensitive information.
Looking ahead: building a collaborative community for open source AI in drug discovery
The AI for Drug Discovery Forum served as an open invitation for researchers, industry professionals, and academic experts to join the AI Alliance working group. This group aims to bring together a community of collaborators to influence the direction of AI development and standards in drug discovery. Supported by researchers from industry and universities, with active interest from the pharmaceutical industry and Massachusetts state government, this initiative signals a promising future for open AI-driven innovation in healthcare.
Those interested in joining the working group or accessing the Drug Discovery project on the Mass Open Cloud (MOC) can sign up for an MOC account to work with the models and tools demonstrated at the event. By uniting resources like Red Hat OpenShift AI and the MOC, this joint effort continues to empower open research and strengthen collaboration, laying the groundwork for impactful advancements in AI-driven healthcare.
