38th ACS National Medicinal Chemistry Symposium

Schrödinger is excited to be participating in the 38th ACS National Medicinal Chemistry Symposium taking place on June 23rd – 26th in Seattle, Washington. Join us for a presentation and workshop by Jennifer Knight, Director at Schrödinger.

Presentation:

Driving Innovation with Machine Learning: Impact on a Pipeline of Drug Discovery Programs (IL27)

Speaker: Dr. Jennifer Knight, Director

Abstract:
Physics-based modeling and machine learning approaches are used widely in our drug discovery programs and collaborations to design new compounds and to model potency and ADMET properties. Here, we present several case studies of machine learning strategies employed in our active drug discovery programs including: using active learning with free energy predictions to efficiently profile large chemical spaces, leveraging experimental data for enhancing ADMET profiles in lead optimization using an interactive ML dashboard and applying de novo design workflows for intelligent molecular core design.

Workshop:

Prioritizing DLK inhibitors for potency, selectivity, and brain-penetration: A digital chemistry design challenge

Host: Wade Miller, Senior Manager

Abstract:
In this hands-on workshop, we will use Schrödinger’s LiveDesign platform to design and triage DLK inhibitors using a series of predictive models:

• Interactive 2D/3D design with Ligand Designer
• E-Sol for Kpu,u prediction https://pubs.acs.org/doi/10.1021/acs.jcim.3c00150
• Machine learning-based model for pKa https://pubs.acs.org/doi/10.1021/acs.jctc.3c00044
• Machine learning models trained on both experimental and physics-based data

The DLK program was driven by the models listed above, as well as FEP+ models for both on-target and off-target potency.

The participant who has the best design, as determined by the project MPO, will receive a free seat to one of Schrödinger Online Certification Courses.