As an individual brimming with ambition and drive, I am thrilled to embark on the next phase of my illustrious career as I approach my graduation in the Spring of 2023 . My extensive interests in the cutting-edge fields of molecular dynamics (MD) simulation and molecular modeling (MM), computational fluid dynamics (CFD), Artificial Intelligence (AI), and machine learning (ML) have fueled my passion to not just excel but to revolutionize in these domains. My fervor to make a meaningful impact in the industry has led me to seek out dynamic and innovative organizations where I can put my skills and expertise to the test. Through a combination of rigorous coursework and hands-on projects, I have not only honed my abilities but also gained invaluable experience that has shaped me into a well-rounded and adaptable candidate, ready to hit the ground running in a diverse array of roles. I am confident in my ability to leave a lasting impression in any position within these fields and am eagerly looking forward to exploring the various opportunities available to me.
Over the past few years, I have devoted myself to the pursuit of excellence in the field of pharmacutics, biopharmacutics, nanoparticles and biotherapeutics. My extensive experience in engineering biologics using state-of-the-art molecular modeling software such as GROMACS, CHARMM, Schrödinger, VMD, and Pymol, has enabled me to push the boundaries of what is possible in this field. I am fluent with several modern programming languages such as Python and Matlab, along with my ability to implement novel computational methods to solve complex research challenges, has further cemented my position as a leader in my field. My expertise in developing and implementing computational strategies to guide the design of drug and small molecules with desired biological and development characteristics, has been honed through years of experience working within multi-disciplinary teams to apply computational chemistry techniques, cheminformatics, and diverse molecular modeling approaches for the purpose of executing chemical screening campaigns, supporting medicinal chemistry activities in drug design, and advancing small-molecule drug discovery programs. My knowledge of computational biophysics and computational chemistry, as well as my experience with coarse-grained and all-atom molecular dynamics, has been instrumental in making substantial contributions to the field of computational science. I am honored to have played a part in advancing the state of the art in this field and am eager to continue my research and make even more impactful contributions in the future.
tibo.duran@uconn.edu
Department of Pharmacutical Sciences
University of Connecticut
69 N Eagleville Rd
Storrs
United States
Research Interests:
Molecular Dynamcs
Molecular Modeling
Drug Discovery, Development, and Design
Nanoparticles
Machine Learning
Education
Ph.D., University of Connecticut, Expect 2023
M.S.,University of Dayton, 2015
Selected publications
Peer-reviewed- Coarse-grained Molecular Coarse-grained Molecular Dynamics Simulations of Paclitaxel-loaded Polymeric Micelles
Duran, T., Costa, A., Gupta, A., Xu, X., Zhang, H., Burgess, D.; and Chaudhuri, B.
- Molecular Dynamics Modeling Based Investigation of the Effect of Freezing Rate on Lysozyme Stability
Duran, T., Minatovicz, B., Bellucci, R., Bai, J., and Chaudhuri, B.
- Molecular dynamics simulation to uncover the mechanisms of protein instability during freezing
Duran, T., Minatovicz, B., Bai, J., Shin, D., Mohammadiarani, H., and Chaudhuri, B.
- CHARMM-GUI Synthetic Polymer Modeler for Modeling and Simulation of Synthetic Polymers.
Choi, Y.Kyo., Duran, T., and Im, W.
- Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing
Sansare, S., Duran, T., Mohammadiarani, H., Goyal, M., Yenduri, G., Costa, A., ... and Chaudhuri, B.
- On the Applicability of the Coarse Grained Coupled CFD-DEM Model to Predict the Heat Transfer During the Fluidized Bed Drying of Pharmaceutical Granules.
Aziz, H., Sansare, S., Duran, T., Gao, Y., and Chaudhuri, B.
Selected Projects
Molecular dynamics simulation to uncover the mechanisms of protein instability during freezing
Freezing MD simulation system including theroputic protein LDH, ice seed, sodium phosphate buffer and water molecules. The activity and movement of protein LDH under freezing condition.
The thermodynamic stability of protein LDH (tetramer protein) under COM pulling MD simulations using Martini coarse-grained model. Protein-protein interaction and aggregation under different process temperatures using Martini coarse-grained model.
Coarse-Grained Molecular Dynamics Simulations of Paclitaxel-Loaded Polymeric Micelles
Amphiphilic block co-polymer PEG-PLA self-assembly to form blank (left) and paclitaxel drug (right) loaded polymeric micelles using Martini model.
The evaluation of formed micelle thermodynamic stability (left) and drug release mechanism (right) using COM pulling MD simulations.
Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing
Artificial Neural Networks (ANN) based graphic user interface (GUI) to assist the end-user in performing interactive simulated risk analysis and visualizing model predictions in nanoparticle formulation process.
On the Applicability of the Coarse Grained Coupled CFD-DEM Model to Predict the Heat Transfer During the Fluidized Bed Drying of Pharmaceutical Granules
A coarse grained coupled CFD-DEM method of modeling the momentum and heat transfer during the fluidization of pharmaceutical granules. The heat transfer during the fluidization of large number of particles was predicted by simulating a smaller number of bigger particles with appropriate scaling of particle–particle heat and momentum transfer, and particle–fluid heat and momentum transfer at significantly smaller computational time.