“Generative AI has the potential to accelerate many industries, including healthcare and drug discovery. There is a desire to apply Generative AI to anything that is digital. Working at the intersection of biology and computing may be the most exciting new spot for technologists at the moment. With 90% of the world’s 97 zettabytes of data generated in the past 2 years and 30% of today’s data being healthcare related, how are we using data technology and AI to advance our understanding of disease and deliver “breakthroughs that change patients’ lives?”. Data, information, and knowledge are as critical as algorithms in drug discovery.
Drug discovery is experiencing a fundamental shift in terms of evolving from in silico to in machino, that is, predictive AI to Generative AI. In machino which is synonymous to Generative AI can be used to design new chemical compounds that have never been synthesized. This can lead to the discovery of new drugs with unique properties and potentially be used to treat diseases that currently have no effective treatments. We expect this evolution to continue at a rapid clip for the foreseeable future.
In this presentation, we will explore how the intersection of generative AI with drug discovery is driving breakthrough innovations that could help how we treat diseases. We will discuss multiple frameworks and discuss the features of these techniques in terms of generating novel molecules with desired properties.
Key takeaways:
- Generative AI models and their implications for drug discovery and biotechnology.
- Descriptive, predictive, and prescriptive analytics of biomedical data.
- Tools, technologies, and languages that AI scientists are using to gain extra insights and value from biomedical data.
- Benchmarking these Generative AI methods for de novo molecular design.”
