Designs, develops, and applies new, customized computer models/tools and applies, where necessary and available, commercial computer modeling software
Contributes to research projects and programs focused on development of specific synthetic molecule drug formation mechanism for clinical and commercial use.
Contributes to the advancement and overall knowledge of computer modeling within Synthetic Molecule Design and Development, SMDD.
Work may be conducted independently, or more frequently, as part of a product development team consisting of other Chemists, Engineers, analysts, program managers, and technicians.
Understand and apply quantum mechanical methodology to address key questions related to reaction thermodynamics, transition state analyses, and other properties of molecules and processes involved in the reactions.
This research will require the development of robust computational methods in the areas of nitrosamines formation in pharmaceutical products.
The post-doctoral researcher will evaluate the models of nitrosamine formation from structure and explore various mechanisms in the drug product environment.
Understand and integrate computational chemistry methods which leverage machine learning and other artificial intelligence techniques for chemoinformatic predictive tools applied to synthetic processes.
Integrates the overall project modeling objectives with the experimental objectives of the broader product design team.
Requirements :
Ph.D. in Computational Chemistry or Chemistry/Chemical Engineering with 0-2 years of research and development experience in computational chemistry applied to pharmaceutical, materials, chemical, or related industry.
Substantial computational modeling expertise and demonstrated application and/or development of modeling tools.
Deep theoretical understanding and proven application of computational chemistry frameworks such as density functional theory, molecular dynamics, and/or meta-dynamics, towards understanding and prediction of organic synthesis reactions.
Strong knowledge of modeling molecules in acidic/basic reaction condition environments.
Knowledge of performing chemical reactions over solid state surface.
Strong knowledge of reaction mechanism, substituent effect, effect of stereo electronic effects will be highly desirable.
Experience with molecular modeling software such as Gaussian, TurboMole, Cosmotherm, Gromacs, LAMMPS, and Schrodinger is ideal.
Proficient in programming, scripting, and pipelining using R, Python, KNIME, and/or Pipeline Pilot is preferred.
Experience in submitting simulations in a High-Performance Computing (HPC) environment.
Colonel Eli Lilly established Lilly in 1876 to produce high-quality medications that addressed actual medical requirements during a time when dubious individuals were selling dubious elixirs. He gave the following directive to the generations of workers: "Take what you find here and make it better and better."
We are still dedicated to his vision over 145 years later, and it permeates every facet of our company and the people we serve, from individuals who use our medications to medical professionals, staff, and the communities we live in.
The antipsychotic drug Zyprexa (olanzapine) (1996), the clinical depression medications Prozac (fluoxetine) (1986), and Cymbalta (duloxetine) (2004) are well-known Lilly products as of 2022; however, its main sources of income are the diabetes medications Humalog (insulin lispro) (1996) and Trulicity (dulaglutide) (2014). Among Lilly's accomplishments are being the first business to mass-produce insulin and the Jonas Salk polio vaccine. It was among the first pharmaceutical companies to use recombinant DNA to generate human insulin, such as Basaglar (insulin glargine), Humulin (insulin medicine), and Humalog (insulin lispro), the first biosimilar insulin product to be licensed in the United States.
Since the day Colonel Eli Lilly opened the doors to our first laboratory in Indianapolis, this spirit has served as our defining characteristic. After more than 140 years and almost 100 medications, we continue to search for the next big finding and further methods to improve the lives of people everywhere.