2023 - Using Machine Learning to Find High Potential Lead Molecules

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.