Using Machine Learning to Find High Potential Lead Molecules, Upcoming Webinar Hosted by Xtalks
Tuesday, May 23, 2023
In this free webinar, learn how the trajectory of the drug discovery landscape is changing in response to the emerging, next generation technologies. Attendees will gain insights into how data-informed and AI-guided chemical library design opens the door for small molecules to address "undruggable" targets and novel modalities. The featured speakers will discuss how leading-edge machine learning and cheminformatics tools enable medicinal chemists to identify the most advanceable lead molecules and optimize for clinical success.
TORONTO, May 15, 2023 /PRNewswire-PRWeb/ -- The promise of machine learning to dramatically reduce timelines in a lengthy drug discovery process is exciting, but it relies on the ability to generate a trusted set of empirical data and the continuous iteration of models.
In this webinar, the featured speakers will demonstrate their iterative and machine learning-driven discovery engine across multiple disease targets to predict novel, active chemical scaffolds and recognize SARs otherwise not identified by public data alone. Using a rapid combinatorial library and microfluidic high-throughput screening technology, it is possible to quickly design, build and screen molecular libraries to discover bioactive molecules (and inactive molecules) with quality dose-response data. This dose-response data allows library-wide SAR characterizations, empowering deep-learning QSAR model predictions and novel lead generation. The result is core starting points and activity-predictive models that expedite decision-making steps during chemical series progression.
The frontier of medicine is often fraught with early discovery decisions based on limited empirical data. While biologics discovery has been accelerated by leveraging nature's tools for site-selective mutagenesis and rapid maturation or optimization, small molecule discovery is in desperate need of new tools that will similarly allow rapid learning cycles and optimization of clinically relevant drug properties.
In this webinar, the speakers will explore how empirical and computational platforms scout chemical space, generate measures of SAR and map the critical structural patterns to predict activity and guide development. They also explore the molecular space not previously examined and create predictions to streamline optimization toward higher potential lead molecules.
Join this webinar to learn how custom chemistry and machine learning bring medicines to patients faster.
Join experts from 1859, Ghotas Evindar, PhD, SVP Discovery; Andrew MacConnell, PhD, Cofounder and Scientific Fellow; and Hossam Ashtawy, PhD, Director of AI and ML, for the live webinar on Thursday, May 25, 2023, at 11am EDT (4pm BST/UK).
For more information, or to register for this event, visit Using Machine Learning to Find High Potential Lead Molecules.
ABOUT XTALKS
Xtalks, powered by Honeycomb Worldwide Inc., is a leading provider of educational webinars to the global life science, food and medical device community. Every year, thousands of industry practitioners (from life science, food and medical device companies, private & academic research institutions, healthcare centers, etc.) turn to Xtalks for access to quality content. Xtalks helps Life Science professionals stay current with industry developments, trends and regulations. Xtalks webinars also provide perspectives on key issues from top industry thought leaders and service providers.
To learn more about Xtalks visit http://xtalks.com
For information about hosting a webinar visit http://xtalks.com/why-host-a-webinar/
Contact:
Vera Kovacevic
Tel: +1 (416) 977-6555 x371
Email: vkovacevic@xtalks.com
Media Contact
Vera Kovacevic, Xtalks, +1 (416) 977-6555 x371, vkovacevic@xtalks.com
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