- ML and AI’s transformative impact in life sciences explored in an upcoming webinar hosted by Xtalks.
- Challenges include data quality, ethics, and infrastructure; FAIR data principles and the Cambridge Structural Database (CSD) presented as solutions.
- CSD, with 1.28M curated structures, exemplifies FAIR principles, certified by CoreTrustSeal.
- Experts from AstraZeneca and CCDC to discuss challenges and importance of quality data management.
- Webinar offers insights into how FAIR data principles and resources like CSD can revolutionize life sciences.
Main AI News:
The transformative potential of machine learning (ML) and artificial intelligence (AI) in life sciences is the subject of an upcoming webinar hosted by Xtalks. This event promises to deliver valuable insights into the challenges and opportunities presented by ML and AI in various industries, including healthcare, green energy, and technology.
One of the key challenges discussed in the webinar is the quality and availability of data, which are fundamental for training and validating ML models. Without reliable data, the full potential of ML and AI in life sciences cannot be realized. To address this issue, the webinar highlights the importance of adhering to the findable, accessible, interoperable, and reusable (FAIR) data principles, which aim to enhance data’s machine-actionability.
A shining example of FAIR data principles in action is the Cambridge Structural Database (CSD), which contains over 1.28 million fully curated small-molecule organic and metal–organic crystal structures. This vast repository of high-quality data is designed to be interoperable and is certified by CoreTrustSeal, making it a trusted resource for academic and industrial institutions worldwide.
The webinar will feature experts such as Dr. Ola Engkvist from AstraZeneca, Dr. Jonathan Betts, and Dr. Clare Tovee from the Cambridge Crystallographic Data Centre (CCDC). These thought leaders will discuss the challenges of using ML and AI in life sciences and underscore the importance of quality data management.
Attendees will gain valuable insights into how the FAIR data principles and resources like the CSD can advance ML and AI applications in fields such as drug discovery. By breaking down barriers and fostering collaboration between academia and industry, these technologies have the potential to revolutionize the life sciences industry.
Conclusion:
The webinar highlights the critical role of ML and AI in revolutionizing the life sciences industry. By addressing challenges in data quality and management through FAIR principles and leveraging resources like the CSD, organizations can unlock new opportunities for innovation and collaboration. This signifies a promising outlook for the market, with the potential to drive significant advancements in drug discovery, healthcare, and beyond.
