Director, Computational Sciences

Company Description

About Abb Vie

Abb Vie'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 Abb Vie, please visit us at  Follow @abbvie on Linked In, Facebook, Instagram, X and You Tube.

The Molecular Profiling and Drug Delivery (MPDD) function within the Synthetic Molecule CMC organization is accountable for a broad range of deliverables across multiple stages of drug discovery and development. From virtual screening, lead generation, and lead optimization through candidate selection, MPDD scientists use state-of-the-art automation and computational tools, supported by expertise in biopharmaceutics, drug delivery, and solid-state chemistry, to collaboratively design and advance candidates with a higher probability of success into development and to inform clinical drug delivery strategy.

From candidate selection through clinical proof of concept and product launch, MPDD scientists work on cross-functional teams to identify the commercial solid form of the active pharmaceutical ingredient (API) and establish structure-property-performance correlations that help deliver robust commercial processes and align control strategies across drug substance and drug product.

Computational chemists within Abb Vie’s MPDD organization partner across Development Sciences and Discovery Sciences in three key focus areas: molecular design and profiling, chemical transitions, and materials design across synthetic modalities. Their work supports the design and advancement of compounds, materials, and formulations with optimal develop ability properties from lead optimization through commercial development, aligned with the broader goal of advancing first-in-class and best-in-class clinical candidates.

The team develops hierarchical modeling approaches that integrate physics-based atomistic methods, including molecular dynamics and quantum mechanics, with machine learning, artificial intelligence, and hybrid physics-informed AI/ML models across hardware platforms for precision in predictions. In close collaboration with medicinal chemists, data scientists, materials scientists, formulation scientists, engineers, and molecular modelers, the team embeds these tools into screening funnels across medicinal chemistry design cycles and preclinical and clinical drug-substance and drug-product development, while maintaining a strong partnership ecosystem to source and leverage external innovation.

Abb Vie’s MPDD organization is seeking a visionary leader with a successful track record of innovation and deliverables in a drug discovery setting to head a global computational chemistry team of 10-12 talented scientists and matrixed leadership of broader modeling community across the larger Synthetic Molecules CMC (SM CMC) and Development Sciences Organizations. This leader will report to the Global Head of Molecular Profiling and Drug Delivery organization and be a member of the SM CMC Extended Leadership Team.

In partnership with other key leaders in Discovery Research, Development Sciences and Information Research, they will be responsible for co-creating a forward-looking strategy that realizes the promise of Computational Sciences and AI-enabled drug discovery and development to deliver on a portfolio of new and improved therapies for patients in alignment with Abb Vie’s broader R&D priorities and AI strategy across synthetic modalities.

The ideal candidate will bring a strong record of project impact, capability development, and external partnership building across the mid-to late-stage discovery process, with expertise in quantum mechanics, atomistic molecular simulations, including molecular dynamics, and AI/machine learning. Prior experience mentoring early/mid-career scientists in matrixed and/or direct reporting…