Greetings!
Equal parts scientists and educators, in the Lucent Lab, we strive to blend these facets into all of our endeavors. Our research is focused on solving intriguing problems in biochemistry and biophysics using the tools of computational physics, machine learning, and high-performance computing. Specifically, our work involves studying protein folding, protein design, and small molecule drug design; applying classical and quantum, equilibrium and non-equilibrium statistical mechanics as appropriate.
Lab Members
Dr. Del Lucent
Primary Investigator
I started my academic life as a first generation college student at Wilkes University in my home of Northeastern Pennsylvania studying biology and physics. After Wilkes, I attended Stanford University for my graduate degree. While at Stanford, I studied protein folding and drug design using molecular simulation as part of the folding@home distributed computing project. After receiving my Ph.D., I relocated to Melbourne, Australia to work for the Commonwealth Scientific and Industrial Research Organization (CISRO) as a postdoctoral fellow and bioengineer. While at CSIRO, I used the techniques of computational enzyme design and structural genomics to design enzymes for bioremediation.
After three years in Australia, I returned to my home town, of Wilkes-Barre for a tenure track position at my alma mater, hoping to contribute to the same environment that provided me with so many opportunities.
Research interests
Protein Folding
Protein Folding is the "molecular origami" that is required for nearly all of our cells' molecular machines (proteins) to function. Although the recipe for building our body's proteins is written in our DNA, the process of going from a sequence of parts to a functional protein is incredibly complex. Despite more than a half-century of study, the "protein folding problem" remains unsolved. Although recent breakthroughs in AI have allowed accurate prediction of what a protein will look once it is folded, we still cannot accurately predict how long it will take to fold and when it will fail (let alone why). This failure lies at the heart of many terrible diseases including Alzheimer's Disease and some forms of cancer. We believe that the answer to this question lies in the study of this problem on multiple length and timescales, many of which are only accessible to physics-informed molecular simulation with the aid of powerful computers (or networks of computers such as in the folding@home project).
Computational Drug Discovery
Computational Drug Discovery is the process of using computer simulations to identify or design useful pharmaceutical compounds. Our goal is to develop a deep understanding of the physics behind protein-ligand interactions and use this knowledge to predict the activity of new drug compounds. In our group we employ physics-based methods such as docking, free-energy perturbation, and molecular dynamics along with deep learning and topological data analysis to accomplish this task.
Other Areas
We are committed to the mission of lifelong learning, we enjoy spending time studying other topics with less obvious and immediate applications to computational chemistry, biophysics, and materials science. Of particular interest to our group are artificial intelligence and quantum computing. While these areas have significant practical importance, they also offer a glimpse into the deep foundational scientific questions that have captivated humanity since antiquity.
Teaching interests
My teaching experience spans a variety of physics courses, from non-major popular science to graduate-level physics. I have a particular passion for teaching statistical mechanics, quantum mechanics, and first-year foundation courses. I aim to inspire my students to develop a deep curiosity and a lifelong love for learning, helping them to see the beauty and relevance of physics in everyday life.
