Senior Principal Research Scientist, Computational Drug Discovery Optimization Team Lead

Company Description

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 www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.

Job Description

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. Reports to Head of Computational Drug Discovery (CDD) and will lead the computational Lead Optimization team. Will implement and enable collaborative design between members of the Optimization team and Global Medicinal Chemistry (GMEC) teams. Must possess strong expertise in modern medicinal chemistry, including a deep understanding of modern computational and experimental strategies, property-based drug design, and pharmacology needed to develop low-dose small molecule drug candidates. The ideal candidate will have a strong and established background in drug discovery from exploratory through candidate nomination including an established external footprint, and network as well as a proven track record of successfully leading diverse project teams to identify novel drug candidates. Past experience will have included mentorship of early/mid-career scientists in matrixed and/or direct reporting relationships. 

Responsibilities:

• Consult with project team and GMEC leadership to establish quantitative targets for driving optimization, and assigning an Optimization team member to the project team that can execute these goals for accelerated identification of low dose drug candidates 

• Guide Optimization team members in benchmarking experiments to evaluate models’ performance and domain of applicability 

• Clearly communicate to project teams the rationale behind the selection of specific models and why they are best suited for the task at hand 

• Lead the development of a cohesive collection of internal models and workflows, to comprehensively address AbbVie’s small molecule portfolio needs (suitable models to predict small molecule binding affinities for protein targets, and to predict pharmacological properties (PK parameters, etc.) 

• Partner closely with the GMEC leadership team to identify and address gaps where computational predictions are needed 

• Advance scientific expertise within the Optimization team and collaborates with cross-disciplinary teams from CDD and Information Research to advance programs and ensure computational capabilities are best implemented across programs 

• Proactively demonstrate leadership by providing expert advice and shared knowledge with direct reports, peers, and senior management 

• Identify and integrate emerging scientific trends from internal and external sources into functional objectives 

• Present program updates and research strategies to functional and non-functional leaders as well as potential and existing partners 

• Maintain awareness of emerging literature and science in the field and author scientific disclosures/publications 

• Maintain scientific presence outside AbbVie with appropriate publications and conference abstract submission highlighting innovative learnings in computational medicinal chemistry 

Qualifications

Qualifications

• Bachelor’s Degree or equivalent education in Science related field with extensive (typically > 18 years) of related work; Master’s Degree or equivalent education with extensive (typically > 16 years) of related work; PhD in Science or related field with typically 10+ years of related work experience 

• Highly organized and self-directed, experience leading multiple complex simultaneous research programs or initiatives 

• Extensive knowledge and experience in applying data science approaches for medicinal chemistry optimizati