In collaboration with Discovery Research and Development Sciences project teams, utilize advanced computational tools towards the design, optimization, and profiling of drug candidates.
Serve as a lead computational scientist on project teams and drive the development and implementation of appropriate computational models within projects to support various aspects of drug discovery and drug development.
Generate hypotheses for compound property improvement and apply them to new compound design.
Contribute to the development of computational infrastructure by identification of novel tools and techniques.
Act as a strong team player in a cross-functional and collaborative environment.
Demonstrate excellent oral and written communication skills.
Promote design-driven and predict-first culture
Educate and train colleagues on various computational tools and techniques
Bachelors, Masters, or PhD with 8+ (BS), 5+ (MS), and 0-3 (PhD) years of experience in computational chemistry, computer science, biophysics, chemical engineering, or related field
Knowledge in one or more areas of the computational chemistry discipline, such as quantum mechanics/chemistry, molecular reactivity, molecular dynamics, structure-based design, ligand-based design, structure property relationship modeling, physicochemical property prediction
Advanced user of QM or MD software packages. This may include open-source codes (PySCF, Psi4, GROMACS, OpenMM, LAMMPS, etc..) or commercial software (VASP, Schrodinger, OpenEye, MOE, etc).
Ability to build and integrate computational models through combining different workflows while also modifying or improving workflow source codes as needed.
Familiarity with machine learning or neural network packag
Benefits
Vision insurance
Additional Information
The Molecular Profiling and Drug Delivery (MPDD) function within the Synthetic Molecule CMC organization is accountable for a broad range of deliverables across various stages of drug discovery and development. During virtual screening/lead generation/optimization and through candidate selection, MPDD scientists utilize state of the art automation and computational tools supported by expertise in biopharmaceutics, drug delivery, and solid-state chemistry to collaboratively design and progress candidates with higher probability of success into development and advise clinical drug delivery strategy. From candidate selection through clinical proof of concept and product launch, MPDD scientists work in cross functional teams to identify the commercial solid form of the active pharmaceutical ingredient (API) and establish structure-property-performance correlations to help deliver robust commercial processes and align control strategies across drug substance and product.
Computational chemists within AbbVie's MPPD organization work collaboratively with other functions within Development Sciences and Discovery Sciences across two focus areas: molecular design and profiling and formulation design across modalities. MPDD computational chemists guide the design and progression of compounds and formulations with optimal developability properties towards the overall vision of advancing first-in-class and best-in-class clinical candidates. They focus on developing hierarchical modeling approaches, including physics-based atomistic modeling, encompassing Molecular Dynamics (MD) and Quantum Mechanics (QM), Machine learning (ML)/Artificial Intelligence (AI) and hybrid models based on Physics-ML/AI approaches. They also collaborate seamlessly with medicinal chemists, data scientists, material scientists, and molecular modelers to incorporate these models in project screening funnels and medicinal chemistry design cycle.
AbbVie's MPDD organization is seeking a highly motivated, talented, and creative scientist with experience and expertise in computational chemistry for a Senior Scientist position. This person will make key contributions towards enablement of our modeling vision. Specifically, the person will be working collaboratively with discovery colleagues to develop stage-appropriate computational models using advanced computational techniques to enable design and optimization of drug candidates across modalities. The ideal candidate should possess an advanced degree in computational chemistry with a strong background in the area such as quantum mechanics, atomistic molecular simulations (molecular dynamics) and AI/machine learning.
Abbvie · North Chicago, IL