Senior Scientist I - II, Computational HitGen / LeadGen

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.
Senior Scientist I: Degree in relevant field (chemistry / computer science / machine learning / cheminformatics / chemical engineering) with experience developing machine learning models related to chemical and biological data (10+ years of experience for BS, 8+ years for MS, 0+ years for PhD)
Senior Scientist II: Degree in relevant field (chemistry / computer science / machine learning / cheminformatics / chemical engineering) with experience developing machine learning models related to chemical and biological data (12+ years of experience for BS, 10+ years for MS, 4+ years for PhD)
Knowledge and experience in modern computational approaches for medicinal chemistry
Expertise using cutting-edge computational approaches built on Machine Learning/Deep learning and Cheminformatics
Ability to implement, debug, and maintain computational tools in common programming languages (e.g., Python)
Familiarity with AI/ML-enabled molecular generation, pose prediction, affinity prediction and/or prediction of pharmacological properties (e.g. PK)
Familarity with retrosynthetic analysis, to contribute to refining workflows for generation/prioritization of novel molecules on the basis of synthetic tractability
Strong analytical and problem-solving skills with demonstrated ability to think critically and creatively, and provide solutions both individually and collaboratively with internal experts
Excellent ability to communicate clearly and concisely with colleagues and collaborators including an ability to explain complex ideas to non-specialists
Preferred: has contributed to molecular design in an early-stage drug discovery campaign (hit gen / lead gen)

Employment type
Full-time

Work arrangement
No