Computational Modeling & AI Focus Day | Tuesday, May 20
8:00 am Check-In & Light Breakfast
9:00 am Chair’s Opening Remarks
Optimizing Computational Modeling of Dynamic GPCRs to Accurately Predict Molecular Interactions for Improved Drug Discovery
9:15 am Combining Computational Chemistry & Structural Biology of GPCRs to Guide Small Molecule Design for Metabolic Diseases
Synopsis
- Characterizing GLP1 in different conformational structures with small molecules to understand the downstream pharmacology
- Turbocharging data analysis using computational methods
- How to use data to guide medicinal chemistry downstream
9:45 am Fireside Chat: Harnessing AI & Machine Learning to Accelerate GPCR Drug Discovery
Synopsis
- Exploring the role of AI-based tools in GPCR drug discovery to understand conformation and how small molecules interact with GPCRs
- How can AI be applied to GPCR computational modeling to aid in predicting GPCRs’ inherent flexibility?
- What are the hurdles in predicting ligands for GPCRs?
10:15 am Morning Networking Break
Spearheading GPCR-Targeted Drug Discovery with AI & Machine Learning to Speed Up the Development Process
11:45 am Employing AI & Machine Learning in GPCR Drug Discovery to Unlock the Undruggable Proteome
Synopsis
- Developing a platform for designer proteins as therapies for GPCRs
- Combining deep learning design engine and synthetic biology platform to identify GPCR binders (Yotta ML2)
- Overcoming the intrinsic disorder of GPCRs and modeling binders to the N-terminus
12:15 pm Roundtable Discussion: Unleashing Computational Methods for Virtual Screening of GPCRs Targets to Speed Up Discovery
Synopsis
- How to select the right screening libraries based on the specific characteristics of the target GPCR and intended indication
- What are the appropriate biochemical and biophysical validations for computational screening of GPCRs
- How can we use AI models to prioritize virtual screening hits?
1:00 pm Networking Lunch
Leveraging Machine Learning & Molecular Dynamics to Predict GPCR Activity to Design New Drugs
2:15 pm ML Mapping from GPCR Conformations to Multidimensional Pharmacology
Synopsis
- Our patent-pending ML methods analyze molecular simulations to identify GPCR intracellular pocket conformations and predict signaling efficacy along multiple pathways
- Analysis of ML models pinpoints structural mechanisms of pathway-specific GPCR activation
- Associated orthosteric and allosteric binding site conformations are useful for virtual screening and lead optimization of biased (or unbiased) agonists and antagonists
2:45 pm Integrating Machine Learning with Large-Scale Activity Measurements by Engineered Cells to Discover and Design GPCR-Activating Antibodies
Synopsis
- Implementing distributed activity measurement using FAST engineered cells to identify antibodies that activate GPCRs
- Running data through the FAST machine learning workflow to learn the sequencefunction landscape and then validating discovered and generated candidates
- Focusing on function and purposefully not optimizing for affinity, especially up front in the discovery process, can be a way to uncover GPCR activating antibodies