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 machine learning approaches to guide molecular optimization. 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 quantitative targets for driving optimization.
• Identify suitable models to predict small molecule binding affinities for protein targets, and to predict pharmacological properties (PK parameters, etc.).
• Build predictive models for each project endpoint, and carefully benchmarking to evaluate performance and domain of applicability for each model.
• Decisively identify the model that should be used for each project team query molecule, using data-driven approaches that take account of model uncertainty and heteroscedasticity.
• Clearly communicate to the project team the rationale for model selection and why the selected models are best suited for the task at hand.
• Deploy models to ensure accessibility for the project team, facilitating collaborative ideation and optimization.