Purpose:
This position brings an opportunity to make innovative contributions that will define how AbbVie leverages AI-enabled drug discovery across our small molecule portfolio. The key responsibilities of this position are to identify, develop, optimize, validate, and deploy innovative methods for computational hit generation. This role will collaborate closely with project teams comprising medicinal chemists, structural biologists, biochemists, cell biologists, and other computational chemistry / chemoinformatics experts.
Responsibilities:
- Consult with project team to establish desirable project-relevant hit/lead criteria
- Develop and maintain proficiency with the collection of cutting-edge computational methods available to the HGLG team
- Support project teams by decisively identifying the method(s) that best fit the project goals, and deploying the most suitable method(s) for the task at hand
- Effectively communicate to the project team the rationale for chosen methodologies, explaining how these approaches are optimally suited to the task and how they inform the resulting designs for a given target
- Contribute to development of methods / workflows for computational hit generation, including methods to predict binding affinities of diverse chemotypes, to design compounds with specified pharmacological properties (PK parameters, etc.), and to rapidly sample both enumerated and AI-generated chemical spaces. Computational methods to evaluate / optimize synthetic tractability of designs from generative AI are of particular interest for this role.