BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//The Advanced Science Research Center - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:The Advanced Science Research Center
X-ORIGINAL-URL:https://asrc.gc.cuny.edu
X-WR-CALDESC:Events for The Advanced Science Research Center
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260415T120000
DTEND;TZID=America/New_York:20260415T130000
DTSTAMP:20260528T000841
CREATED:20260122T200230Z
LAST-MODIFIED:20260408T152158Z
UID:10001554-1776254400-1776258000@asrc.gc.cuny.edu
SUMMARY:Spring '26 Biochem Seminar: Pratyush Tiwary
DESCRIPTION:AI augmented molecular simulations for predicting protein and RNA structural ensembles\nAI is now everywhere in chemistry\, from structure prediction to molecule generation to automated synthesis. The excitement is real\, but so is the unease about what is genuinely predictive and what is closer to impressive memorization. In this colloquium I will take a statistical physicist’s perspective and use examples from my group’s work to argue for cautious\, but clear\, enthusiasm for AI in chemistry and allied fields. I will show how we combine generative AI with statistical mechanics to learn Boltzmann weighted ensembles from limited training data\, and then extrapolate across temperature\, pressure\, and other thermodynamic conditions reducing the need for explicit\, expensive simulations or experiments. I will highlight the breadth of these methods through applications that include nucleation of crystal polymorphs under nanoconfinement\, prediction of protein and RNA structural ensembles\, and conformation selective drug discovery efforts aimed at Alzheimer’s disease and hypertension. Time permitting\, I will discuss briefly what I think are the biggest challenges facing chemistry research and education as we proceed with the perhaps inevitable adoption of AI. \nPlease use this link to access Zoom. \nFor any questions\, please contact Hyacinth Camillieri at hcamillieri@gc.cuny.edu
URL:https://asrc.gc.cuny.edu/event/spring-26-biochem-seminar-pratyush-tiwary/
LOCATION:ASRC Auditorium\, 85 St. Nicholas Terrace\, New York\, NY\, 10031\, United States
CATEGORIES:Structural Biology
ATTACH;FMTTYPE=application/pdf:https://asrc.gc.cuny.edu/wp-content/uploads/media/event/spring-26-biochem-seminar-pratyush-tiwary/20260415_tiwary_flyer.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260429T120000
DTEND;TZID=America/New_York:20260429T130000
DTSTAMP:20260528T000841
CREATED:20260122T200333Z
LAST-MODIFIED:20260416T195618Z
UID:10001555-1777464000-1777467600@asrc.gc.cuny.edu
SUMMARY:Spring '26 Biochem Seminar: Abhishek Singharoy
DESCRIPTION:Inverting Biophysics: From Function to Ensembles \nMost of computational biology is predicated upon the sequence → structure → function → phenotype paradigm. Thanks to artificial intelligence and the availability of data at various scales\, researchers have been trying to bridge gaps between the different tiers of this process\, starting from the age-old genotype–phenotype modeling to CASP and Alphafold’s sequence-structure up to recent attempts to go from sequence to ensemble. However\, physical causality is often missing in the traditional bioinformatic models\, thus far sidelining the AIdriven advances only to predictions of the forward direction. The lecture will introduce physical ideas to conceive generative models that backmap phenotypes down to an ensemble of structures and sequences. For example\, leveraging our work on modeling the diffusion of charge carriers in bioenergetic membranes\, we computed the mechanism of chemokine binding to the Oxford CovidVaccine. With AstraZenaca\, we computationally redesigned the adenovirus vector to prevent potential clotting disorders. Using Google’s inception network algorithm\, we invert this immune recognition function into a generalizable learning strategy of electrostatic structures across proteins. We are now using this electrostatic network to study disease association in patients\, as well as design peptide therapeutics\, and search of hidden toxins\, covering the entire human proteome\, generalizing the molecular function-to-ensemble paradigm. \nPlease use this link to access Zoom. \nFor any questions\, please contact Hyacinth Camillieri at hcamillieri@gc.cuny.edu
URL:https://asrc.gc.cuny.edu/event/spring-26-biochem-seminar-abhishek-singharoy/
LOCATION:ASRC Auditorium\, 85 St. Nicholas Terrace\, New York\, NY\, 10031\, United States
CATEGORIES:Structural Biology
ATTACH;FMTTYPE=application/pdf:https://asrc.gc.cuny.edu/wp-content/uploads/media/event/spring-26-biochem-seminar-abhishek-singharoy/20260429_singharoy_flyer.pdf
END:VEVENT
END:VCALENDAR