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Add to Calendar 4/15/2019 3:00 pm 4/15/2019 4:00 pm America/Chicago “Machine Learning in Molecular Systems: From Conformational Changes in GPCRs to Small Molecule Discovery DESCRIPTION:

G-Protein Coupled Receptors (GPCRs), comprises over one-third of the targets of all FDA-approved drugs. One such GPCR, the µ Opioid Receptor (µOR), epitomizes the benefits and drawbacks of existing GPCR drugs. Opioid chronic pain medications, such as morphine and hydrocodone, are µOR agonists that achieve their main therapeutic aim of analgesia, yet cause severe side effects, such as respiratory depression and addiction. Conformational changes of these receptors and intermediate states play a key role in biased agonism. To this end, we used hundreds of MD simulation trajectories and machine learning (ML) to elucidate the conformational changes in Opioid receptors during their activation. The signal transduction pathways and ligand-directed conformational changes will be discussed.

 

In the second part, I’ll talk about different ML algorithms used in molecule/material discovery and present their accuracies for different tasks. The limitations of these algorithm along with their interpretability will also be discussed.

 

\n\nSPEAKER: Professor Amir Barati Farimani
Beckman - 3269 false

“Machine Learning in Molecular Systems: From Conformational Changes in GPCRs to Small Molecule Discovery

Speaker Professor Amir Barati Farimani
Date: 4/15/2019
Time: 3 p.m. - 4 p.m.
Location: Beckman - 3269
Event Contact: Donna Fackler
217-300-8022
dfackler@illinois.edu
Cost: No Cost
Sponsor: Theoretical and Computational Biophysics Group
Event Type: Seminar/Symposium
 

G-Protein Coupled Receptors (GPCRs), comprises over one-third of the targets of all FDA-approved drugs. One such GPCR, the µ Opioid Receptor (µOR), epitomizes the benefits and drawbacks of existing GPCR drugs. Opioid chronic pain medications, such as morphine and hydrocodone, are µOR agonists that achieve their main therapeutic aim of analgesia, yet cause severe side effects, such as respiratory depression and addiction. Conformational changes of these receptors and intermediate states play a key role in biased agonism. To this end, we used hundreds of MD simulation trajectories and machine learning (ML) to elucidate the conformational changes in Opioid receptors during their activation. The signal transduction pathways and ligand-directed conformational changes will be discussed.

 

In the second part, I’ll talk about different ML algorithms used in molecule/material discovery and present their accuracies for different tasks. The limitations of these algorithm along with their interpretability will also be discussed.

 

To request disability-related accommodations for this event, please contact the person listed above, or the unit hosting the event.

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