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SUMMARY:Data-Driven Strategies for Organic Structure-Property and Structure
 -Reactivity Relationships
LOCATION:Chemistry A101
TZID:America/Denver
DTSTART:20240404T160000
UID:2026-04-29-21-54-42@natsci.colostate.edu
DTSTAMP:20260429T215442
Description:About the Seminar: \n\nThe prediction of molecular properties 
 plays a pivotal role in various domains\, from drug discovery to materials
  science. With the advent of machine learning techniques\, particularly in
  the field of cheminformatics\, the prediction of properties for small org
 anic molecules has witnessed significant advancements. This talk delves in
 to the diverse machine-learning strategies employed for the accurate predi
 ction of properties crucial for understanding molecular behavior. In this 
 first part\, I will highlight the development of a new radical stability m
 etric and its utility in identifying novel radicals for aqueous redox flow
  batteries using graph neural networks and reinforcement learning. In the 
 subsequent section\, I will delve into the advancement of graph-based mode
 ls tailored for predicting molecular properties\, accompanied by an in-dep
 th analysis of two specific applications. While the first study deals with
  bond dissociation energy for applications in the pharmaceutical/atmospher
 ic\, the second focuses on predicting descriptors for one of the most wide
 ly used reactions in medicinal chemistry – amide coupling reactions. In 
 the final segment\, I will showcase how computational tools can be combine
 d to make modular workflows for FAIR (Findable\, Accessible\, Interoperabl
 e\, and Reusable) chemistry to enable the usability and reproducibility of
  scientific data. By synthesizing insights from various studies\, this tal
 k aims to provide a comprehensive understanding of how machine-learning st
 rategies are reshaping the landscape of molecular property prediction\, an
 d fostering innovation in drug design\, materials science\, and beyond. 4:
 00 pm
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