Mobley Lab Grad Student Caitlin C. Bannan Receives NSF MolSSI Software Fellowship

Mobley Lab Grad Student Caitlin C. Bannan Receives NSF MolSSI Software Fellowship

PhD Candidate Caitlin C. Bannan from the Mobley Lab received a 2018 Phase-I Software Fellowship for the development of an open source tool using Bayesian inference to automatically sample chemical perception for parameterization of molecular mechanics force fields. The award was granted by the Molecular Sciences Software Institute (MolSSI), a nexus for science, education, and cooperation serving the worldwide community of computational molecular scientists.

Caitlin has worked in the Mobley Lab for the past three years developing computational methods to help with drug design. “I am trying to teach computers how to learn chemistry so that we can automate how this chemical perception is chosen,” explains Caitlin. “The MolSSI Fellowship will allow me to continue to develop tools in which a computer can learn the necessary chemical perception for a force field using a set of molecules and experimental or quantum mechanical data for those molecules. Any code I generate as a part of this project will be available open source to the computational chemistry community.”

When asked what she enjoys most about her work, Caitlin shared how she likes being able to automate processes people have traditionally done by hand. “Sometimes this is easy, just piecing together tools that other people have already developed,” says Caitlin. “This chemical perception project takes this to the next level, it is a completely new way of apply machine learning and cheminformatics. I think the future of most fields, including drug discovery, depend on taking advantage of the automation computers can give us. Scientists will never be replaced by these tools, but more automation allows scientists to focus on bigger and more unique problems.”

Learn more about Caitlin’s research by watching her Spotlight Video:

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