Senior Scientist I Computational Chemistry

About the role

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, and optimization 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 a 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 MPDD 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 toward 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 (Molecular Dynamics and Quantum Mechanics), Machine Learning (ML)/Artificial Intelligence (AI), and hybrid models based on Physics-ML/AI approaches.

Responsibilities

• In collaboration with Discovery Research and Development Sciences project teams, utilize advanced computational tools toward the design, optimization, and profiling of drug candidates.
• Advance computational chemistry capabilities through innovation and implementation of new methodologies and workflows to capture key developability properties such as permeability, stability, toxicity, and solubility. Examples include utilizing molecular simulations/QM approaches, coupled with AI/ML approaches, to predict dynamic molecular conformations/electronic structure and their impact on Absorption, Distribution, Metabolism, and Excretion, Toxicity (ADMET) properties and Chemistry, Manufacturing and Controls (CMC) properties.
• 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 identifying 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 a design-driven and predict-first culture.
• Educate and train colleagues on various computational tools and techniques.

Qualifications

• 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, including 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 packages including but not limited to Scikit-learn, PyTorch.
• Proficient in writing code in Python, R, C/C++, etc.
• Nice to have: deep understanding of electronic structure methodologies and algorithms, as well as experience with developing quantum circuits/algorithms for quantum computers (e.g., Qiskit expertise).

Benefits

• Comprehensive package of benefits including paid time off (vacation, holidays, sick).
• Medical, dental, and vision insurance.
• 401(k) retirement plan for eligible employees.
• Eligibility to participate in short-term incentive programs.

Pay

The compensation range for this role is based on the job grade and may vary depending on geographic location and other factors. The range may be modified in the future. Individual compensation will be determined based on these considerations.