Using fast combinatorial libraries and microfluidic high-throughput screening technologies to rapidly design, construct, and screen molecular libraries to discover bioactive (and inactive) molecules with high-quality dose-response data I can.
Toronto (PRWEB)
May 15, 2023
The promise of machine learning to dramatically shorten the timelines of the lengthy drug discovery process is exciting, but it relies on the ability to generate reliable empirical data sets and continuous iteration of models.
In this webinar, featured speakers will demonstrate an iterative, machine-learning-driven discovery engine across multiple disease targets to predict novel active chemical scaffolds and recognize SARs not identifiable from public data alone . Using fast combinatorial libraries and microfluidic high-throughput screening technologies to rapidly design, construct, and screen molecular libraries to discover bioactive (and inactive) molecules with high-quality dose-response data I can. This dose-response data enables library-wide SAR characterization, enabling deep learning QSAR models to predict and generate new leads. The result is a core starting point and activity predictive model that accelerates the ongoing decision-making steps of the chemistry series.
On the medical front, early detection decisions often need to be based on limited empirical data. While biologics discovery has been accelerated by harnessing nature’s tools for site-selective mutagenesis and rapid maturation or optimization, small-molecule discovery requires equally rapid There is a desperate need for new tools that enable optimization of learning cycles and clinically relevant drug properties.
In this webinar, speakers will explore how empirical and computational platforms explore chemical space, generate SAR measurements, map important structural patterns to predict activity, and guide development. increase. We also explore previously unexplored molecular space and create predictions to streamline optimization towards more potential lead molecules.
Join this webinar to learn how custom chemistry and machine learning can get medicines to patients faster.
Join the 1859 expert, Dr. Ghotas Evindar, Senior Vice President of Discovery. Dr. Andrew McConnell, Co-Founder and Scientific Fellow. AI and ML Director Dr. Hossam Ashtawi will be on stage for his webinar live on Thursday 25th May 2023 at 11am EDT (4pm BST/UK).
For more information, or to register for this event, see Finding High Potential Lead Molecules Using Machine Learning.
About XTALKS
Xtalks by Honeycomb Worldwide Inc. is a leading provider of educational webinars to the global life sciences, food and medical device communities. Each year, thousands of industry players (life sciences, food and medical device companies, private and academic research institutions, medical centers, etc.) rely on Xtalks to access quality content. Xtalks helps life sciences professionals stay up to date with industry developments, trends and regulations. Xtalks webinars also provide perspectives on key issues from top industry thought leaders and service providers.
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contact:
Bela Kovacevich
Phone: +1 (416) 977-6555 x371
Email: vkovicevic@xtalks.com
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