Senior Scientist I - II, Computational HitGen / LeadGen

Senior Scientist I - II, Computational HitGen / LeadGen Full-time Salary Min: 109500 Salary Max: 208500 Workday Global Grade: 18 Compensation: USD 109,500 - USD 208,500 - yearly About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . Follow @abbvie on LinkedIn, Facebook , Instagram , X and YouTube. 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) Applicable only to applicants applying to a position in any location with pay disclosure requirements under state orlocal law: The compensation range described below is the range of possible base pay compensation that the Companybelieves ingood faith it will pay for this role at the timeof this posting based on the job grade for this position.Individualcompensation paid within this range will depend on many factors including geographic location, andwemayultimatelypaymore or less than #J-18808-Ljbffr ABBVIE