Changing the Landscape of Screening: AI-Powered ASMS Ligand Identification
In the race of accelerated drug discovery, the integration of artificial intelligence (AI) and machine learning (ML) with advanced screening technologies is transforming the way we identify hits. Join us for an insightful session exploring how AI/ML-driven enrichment is improving efficiency of affinity selection mass spectrometry (ASMS) approach in terms of timelines and costs.
This webinar will showcase how cutting-edge computational approaches are being applied to ASMS screening workflows to accelerate hit identification, uncovering novel binders with greater precision. Our expert speakers will walk through real-world applications, share performance metrics and discuss how these innovations can help re-shape early-stage discovery.
Whether you're a scientist, data analyst or decision-maker in biotech or pharma, this session will provide actionable insights into harnessing AI/ML for smarter, faster and highly confident hit discovery.
Attend this webinar to:
- Understand how AI/ML technologies are integrated into ASMS workflows
- Evaluate the impact of AI/ML-driven enrichment on screening efficiency
- Gain knowledge of real-world application of AI/ML in hit discovery
Presenter: Ghislaine Marchand (Group Leader Biophysics, Evotec)
Ghislaine Marchand leads the biophysics group at Evotec Toulouse, bringing over 25 years of expertise in the pharmaceutical industry. She began her career at Sanofi, where she held various R&D positions spanning pharmacokinetics, anti-infective research and high-throughput screening.
Presenter: Benedikt Bauer (Group Leader In Silico R&D at Evotec)
Benedikt Bauer leads a multidisciplinary data science group at Evotec Hamburg, driving the design and implementation of advanced analytics and machine learning models integrated into operational workflows. Since joining Evotec close to a decade ago, Benedikt has shaped various data related aspects of the company, from data management over data engineering to data science. His primary focus is on coordinating closely with biologists to identify scientific challenges where data science can deliver meaningful impact – bridging experimental insights with computational innovation.
