Senior Director, AIRx Computational Chemistry

Takeda•Boston, MA

2d•Hybrid

About The Position

Takeda Research is building a Lab of Tomorrow powered by AI, automation, and new working methods. The AI Research Accelerator (AIRx) unit is a key part of this initiative, designed to function with the autonomy of a biotech company while leveraging the resources of a major pharmaceutical firm. AIRx aims to develop future AI-driven operating models and accelerate the delivery of drug candidates to the clinic. The AIRx Computational Chemist is central to this AI-first model, driving in silico molecular design, utilizing predictive models, and applying generative chemistry to expedite Design-Make-Test-Analyze (DMTA) cycles. This role is critical for achieving the speed and precision that define the AIRx incubator, with computational chemistry serving as a primary decision-making function that directly influences synthesis priorities and clinical progression.

Requirements

PhD in computational chemistry, cheminformatics, biophysics, or a closely related discipline.
12–15+ years of drug discovery experience with a demonstrated track record of computational impact on programs.
Proficiency across computational tools: molecular docking, MD simulation, FEP, QSAR/ADMET modeling.
Strong coding skills (Python required).
Experience building and deploying ML models in a drug discovery context.
Experience with structure-based drug design.
Ability to interpret crystallography or cryo-EM data.

Nice To Haves

Experience applying generative AI or large language models to molecular design is preferred.
Collaborative, communicative, and comfortable at the wet/dry scientific interface.

Responsibilities

Apply generative AI, structure-based design, free energy perturbation, and predictive ML models to design and prioritize chemical matter.
Partner closely with Medicinal Chemists to translate computational outputs into actionable synthesis priorities, fostering a collaborative environment of mutual challenge and learning.
Lead and oversee virtual screening campaigns, ADMET predictions, and selectivity assessments to inform and accelerate design decisions.
Contribute to building and refining AI/ML model training datasets from AIRx experimental outputs.
Develop and maintain computational workflows that enhance team speed and reproducibility.
Interpret and integrate structural biology data (X-ray, cryo-EM) into computational design strategies.
Communicate complex computational findings clearly to cross-disciplinary colleagues and governance forums.
Stay current with advances in generative AI, protein structure prediction, and ML methods; proactively apply new approaches.