AI can play a crucial role in drug discovery, but the lack of sufficient data in this area remains a significant obstacle. Verseon’s newly developed AI model, VersAI™, elevates AI accuracy in drug discovery, even with sparse data. We will speak with Adityo Prakash, CEO of Verseon International, about the role of AI in the future of drug discovery and how VersAI™ is overcoming these challenges. We will also discuss some of the biggest challenges in using AI in healthcare, including accuracy and tackling sparse data.

Some of the main topics in this interview include:

• VersAI™ has demonstrated significantly lower error rates compared to Google’s AutoML. How does it work, and what specific challenges did your team face during its development?
• Small molecule drug discovery often deals with a vast chemical space but limited empirical data. How does your platform overcome the limitations of available empirical data?
• Ongoing projects within Verseon’s pipeline that leverage VersAI™ to develop novel oncology therapies.
• Traditional AI models require massive datasets and often struggle with predicting properties of novel drug-like structures. How does VersAI™ ensure robust predictions with much smaller datasets?
• AI models have been used to optimize clinical trial design and patient recruitment. How does VersAI™ contribute to this process, especially in the context of rare cancers or patient populations where data is limited?
• Some health professionals are skeptical about using AI in clinical decision-making due to concerns about data reliability and transparency. How do you address these concerns, and what steps are you taking to build trust in your AI solutions?
• As AI models like VersAI™ become more prevalent in drug discovery, what regulatory and ethical challenges do you foresee, and how are you preparing to address them?