January 21, 2020 -- We are thrilled to introduce our next laboratory scientist this month, who works at the intersection of chemistry, drug development, and data science. Rajan Chaudhari, PhD, is a postdoctoral fellow at the University of Texas MD Anderson Cancer Center in Houston.
Tell us how you got involved in your current position or laboratory.
When I was finishing my PhD at University of the Sciences in Philadelphia, I uploaded my CV on postdocjobs.com. Dr. Shuxing Zhang, my principle investigator at MD Anderson Cancer Center, found my profile and asked me if I was interested in joining his lab. At that time, I was looking for opportunities either on the East Coast or the West Coast. Initially, I was a bit hesitant to move to Texas. However, once I read about his research at MD Anderson Cancer Center and discussed the opportunity with people in the industry, I knew that joining Dr. Zhang's lab would be a great decision for my career. In his lab, I would develop not only scientific skills but also management and leadership skills. Today, I do not have a bit of a regret with my decision.
What questions are you asking with your lab work? Or what answers will your experiments help you get?
Drug discovery and development is a costly business. Today, we are generating large biochemical data and the headache is now how to extract reliable and relevant information from such large data. By developing new algorithms, we aim to analyze large datasets and develop more effective but less toxic therapeutics in a more efficient manner.
What will your work contribute to the scientific community and the public at large?
My current research is focused on developing methods to predict polypharmacology and toxicity of the small molecules. In general, these methods are mainly used by other computational scientists. I pay special attention in developing tools that are easy to use and can be interpreted by biologists and chemists on their own. I hope that these tools will have wide applicability and will contribute significantly to the scientific community.
What types of experiments and tests do you typically perform in the lab?
As a computational chemist, I routinely write Python codes to develop and test new machine-learning algorithms to predict targets and side effects of the small molecules. I also collaborate with biologists and chemists on number of drug discovery projects and routinely generate hypotheses for experiments on drug binding affinities, molecular interactions, activities to kill cancer cells, and other cancer therapeutics related experiments.
Do you have a favorite experiment to run or protocol? If so, tell us why?
I enjoy trying out new methods that gets published in our field and if suitable, I implement them in our daily routine. But one of the basic protocols that I love to carry out often is superimposing molecular structures. I like to take full advantage of this method by implementing it in various methods, including protein conformational analysis, quantitative structure-activity relationship, and molecular docking studies. Often, the structures of large protein complexes are available in bits and pieces. When common structures are available between them, I can build a complete model of the biological assembly using superimposition. This helps in gaining important structural information for developing hypothesis for the further experiments. I also want to mention that now we started implementing virtual reality technology for molecular visualization and rational design of cancer therapeutics. So this is quickly becoming my favorite task as well.
What future directions do you hope to pursue in your research career?
I am in a field that allows me to apply my current expertise in all aspects of the drug discovery and development pipeline. My current research is focused on early aspects of the drug discovery pipeline, including target prediction, hit identification, and lead optimization. In the future, I would love to expand my research skills in both ends of the pipeline, i.e., in target selection and preclinical drug development areas.
Do you have any publications or materials that readers can reference if they want to learn more about your work?
During my PhD, I have published on improving protein modeling and PyMine, a tool for data integration and visualization for drug discovery. My publication on identification of novel GLP1R agonist would be a great read on computational drug discovery. During my postdoc studies, I have published on polypharmacology and on cancer therapeutics. I would encourage readers to read our review articles on polypharmacology, especially if you are new to this field. Readers can take a look at my research work on ORCID. More information on our lab research can be found by visiting our website.
If you are interested in learning more about Rajan or his work, he can be reached at email@example.com.