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The National Academies will host a workshop exploring the transformative potential of artificial intelligence (AI) and machine learning (ML) in chemical sciences and drug discovery. Researchers, policymakers, and stakeholders will examine how to address data biases and democratize pharmaceutical research to potentially reduce drug development costs. This AI focused workshop will discuss expanding research capacity, particularly for rare diseases, while exploring challenges such as preventing healthcare disparities and ensuring access to advanced technological resources. Panel discussions will feature thought leaders examining the integration of AI, laboratory automation, and robotics to advance the chemical sciences' role in traditional drug discovery.
Description
A committee of the National Academies of Sciences, Engineering, and Medicine under the auspices of the Chemical Sciences Roundtable will plan and host a hybrid, public workshop in Washington, DC to discuss the various benefits and challenges related to research and development in the chemical sciences for drug discovery and specifically related to using artificial intelligence (AI), machine learning (ML), laboratory automation, and robotics.
The public workshop will feature invited presentations to:
- Discuss how the chemical sciences community defines accessibility and affordability, and the positive role AI tools in chemistry can have in democratizing drug discovery;
- Examine how the integration of AI and ML tools in chemical laboratories with automation and robotics can change the traditionally time-consuming and costly drug discovery process;
- Consider how the anticipated enhanced efficiency in these laboratories could dramatically expand capacity for tackling a broader spectrum of diseases, including rare disorders that have historically been overlooked due to limited commercial viability.
With a focus on the topic from a chemical sciences and technology perspective, the workshop will also draw upon thought leaders from other fields, which could, for example, include discussion of the economic implications of AI-assisted drug discovery and exploring how these advancements might contribute to more affordable medications by streamlining the expensive development process.
A workshop proceedings-in-brief, authored by a rapporteur, will be published after the workshop.
Collaborators
Sponsors
Department of Energy
National Science Foundation
Staff
Michael Janicke
Lead
Kayanna Wymbs
Darlene M Gros
