Principal Research Scientist I, Peptide Design

Company Description

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 – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

AbbVie seeks a knowledgeable, self-motivated, and creative scientist for cyclic peptide design in for computational chemistry / drug discovery. The selected candidate will join our Computational Drug Discovery group within the Discovery Research organization. AbbVie has recently gained the capability to synthesize millions of custom cyclic peptide sequences in a “programmable” manner, and to individually characterize their interaction with a desired protein partner. The candidate will be expected to leverage this opportunity to computationally design novel cyclic peptides as an entry-point to this platform.

The candidate will have the opportunity to develop and advance physics-based modeling as well as AI/ML technologies at the heart of computer-aided peptide design. The candidate should have broad experience with computational techniques with a focus on the state-of-the-art methods in machine learning applied to chemical and biological data. The key responsibilities of this position are to identify, develop, optimize, validate, and deploy innovative methods for modeling cyclic peptides and their interactions with proteins. The candidate should have a working knowledge of chemistry and the ability to work collaboratively with a broad variety of disciplines including other members of our internal computational chemistry and cheminformatics groups to accelerate drug discovery from hit identification through candidate selection.

You will have access to state-of-the-art modeling, simulation, and machine learning technologies as well as computing infrastructure, and be able to leverage rich collections of data to accelerate drug discovery in a fast-paced and creative environment.

Key Responsibilities: