Jones, Dr. Murchtricia

Professor Murchtricia Jones, a native of the United States Virgin Islands, has received her Bachelor of Science in Mathematics from the University of the Virgin Islands in 2015, a Master of Science in Bioinformatics from the University of Michigan in 2017 and PhD in Bioinformatics from the University of Michigan in 2020. Professor Jones’ has had research experience at both Rutgers University and the San Diego Supercomputer Center developing and utilizing computational chemistry methodologies. Professor Jones’ dissertation work focused on utilizing machine learning for the generation of an atom parameterization software for small organic molecules.

Professor Jones is enthusiastic about education and teaching in the next generation of scientists. She is versed in programming in Python, R and Matlab. Her particular interests are focused on Machine Learning and software development in Cheminformatics.

In 2019, Professor Jones was awarded the Promise in Computational Chemistry reward from the American Chemical Society. Much of Professor Jones’ career has been dedicated to increasing diversity in STEM education and working with students from disadvantage socioeconomic backgrounds. Professor Jones was also awarded both the Scholar Activist Award and the Diversity, Equity and Inclusion Scientist Spotlight in 2019 from the University of Michigan.

When not programming or working with students, Professor Jones spends time with her family and volunteering at church.

Publications

Murchtricia K. Charles-Jones, and Charles L. Brooks III. Machine Learning based Multipurpose Atom Typer for CHARMM (ML-MATCH). Manuscript Pending.

Murchtricia K. Charles-Jones, and Charles L. Brooks III. Machine Learning based Multipurpose Atom Typer for CHARMM (ML-MATCH): Application and Validation. Manuscript Pending.

Ding, Xinqiang, Ryan L. Hayes, Jonah Z. Vilseck, Murchtricia K. Charles, and Charles L. Brooks III. "CDOCKER and lambda-dynamics for prospective prediction in D3R Grand Challenge 2." Journal of Computer-Aided Molecular Design (2017): 1-14.

Presentations

Poster Presentation: 38th Annual Pharmacological Symposium 2018, University of Michigan, Ann Arbor MI, “Machine Learning in Silico Discovery: A machine learning based atom parametrization program for molecular mechanics force fields”

Poster Presentation: Intelligent Systems in Molecular Biology 2018, University of Michigan, Ann Arbor MI July 2018, Chicago IL, “Machine Learning in Silico Discovery: A machine learning based atom parametrization program for molecular mechanics force fields”

Poster Presentation: NSF Protein Folding Consortium 2017, University of Michigan, Ann Arbor MI 2018, University of Michigan, Ann Arbor MI, “Machine Learning in Silico Discovery: A machine learning based atom parametrization program for molecular mechanics force fields”

Oral Presentation: NSF Protein Folding Consortium 2017, University of Michigan, Ann Arbor MI 2018, University of Michigan, Ann Arbor MI, “Machine Learning in Silico Discovery: A machine learning based atom parametrization program for molecular mechanics force fields”

Invited Talk: Promise in Computational Chemistry 2019, American Chemical Society Meeting, San Diego, CA, “Machine Learning in Silico Discovery: A machine learning based atom parametrization program for molecular mechanics force fields”

Invited Talk: Tools and Technology Seminar 2018, Department of Computational Medicine and Bioinformatics, University of Michigan, “Cheminformatics and Machine Learning”, “Machine Learning in Silico Discovery: A machine learning based atom parametrization program for molecular mechanics force fields”