Senior Director, AIRx Computational Chemistry

Takeda•Boston, MA

2d•Hybrid

About The Position

Takeda Research is building the Discovery Automation & Robotics (DAR) group, which will focus on AI, automation, and innovative work methodologies to deliver differentiated medicines efficiently. Within DAR, two key units are being established: AI Research Accelerator (AIRx) and The Discovery Automation & Robotics (DAR) group. AIRx will operate with the agility of a biotech company while leveraging the resources of a major pharmaceutical firm. Its goal is to pioneer future AI-driven operating models and accelerate the delivery of drug candidates to the clinic with industry-leading speed and success rates. The AIRx Computational Chemist is central to AIRx's AI-first operating model, driving in silico molecular design, utilizing predictive models, and applying generative chemistry to expedite Design-Make-Test-Analyze (DMTA) cycles, thereby enhancing speed and precision. In AIRx, computational chemistry is a primary decision-making function, with generative proposals directly influencing synthesis priorities and the progression of candidates to clinical trials. This role is highly hands-on, offering a significant opportunity to influence the development of clinical candidates through close collaboration with the broader AIRx design team.

Requirements

PhD in computational chemistry, cheminformatics, biophysics, or closely related discipline
12–15+ years of drug discovery experience with 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 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; challenge and be challenged in equal measure
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 increase 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