Director, AIRx Medicinal Chemistry

Lynn, MA, USA|Onsite

Job Overview

Compensation

Salary

Range $177,000.00 - $278,080.00

Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) plan and company match

short-term disability

long-term disability

basic life insurance

Tuition Reimbursement Program

paid Volunteer Time Off

company holidays

well-being benefits

Sick Time

Paid vacation

Job Description

Takeda is a global, values-based, R&D-driven biopharmaceutical leader headquartered in Japan, dedicated to improving the health and well-being of people worldwide through innovative medicines. As one of the largest pharmaceutical companies, Takeda combines its deep expertise in research with a commitment to patients and cutting-edge technologies. Takeda Research is currently pioneering in the realm of AI and automation by establishing the Discovery Automation & Robotics (DAR) group and the AI Research Accelerator (AIRx) to fundamentally transform drug discovery. These initiatives harness artificial intelligence, machine learning, and automation to accelerate the design and delivery of differentiated medicines to the clinic faster and more cost-effectively than traditional models. This is a hybrid, full-time position based in Cambridge, MA, that offers a competitive U.S. base salary range from $177,000 to $278,080, alongside comprehensive benefits and incentives aligning with Takeda’s commitment to equitable pay and employee well-being.

The role of Director, AIRx Medicinal Chemistry, at Takeda is an exciting and forward-thinking opportunity designed for an experienced medicinal chemist at the forefront of AI-driven drug discovery. This position involves working within the AIRx group, a dedicated team of seasoned drug hunters operating with the agility of a biotech and the robust resources of a global pharmaceutical company. The Director will engage in the pure design of molecules at unprecedented speed using the most advanced AI-powered tools available, focusing on rapid decision-making and expert chemistry intuition.

In this role, the incumbent will critically evaluate generative AI proposals and apply deep structure-activity relationship (SAR) knowledge to design and optimize drug candidates by integrating multi-parameter optimization and ADME considerations. The Director will oversee and strategically guide contract research organizations (CROs) responsible for synthesis, ensuring quality and timely delivery, while embedding considerations regarding selectivity, drug developability, and intellectual property early in the design process. Collaboration with computational chemistry and structural biology teams is key, as is staying up-to-date with emerging chemical innovations and competitive molecules. The role demands not just acceptance of AI outputs but active stress-testing and challenging of computational designs to maximize impact.

This position suits a seasoned drug hunter who flourishes in lean, autonomous, and rapid-paced environments, guided by both deep scientific expertise and a genuine curiosity for the capabilities and limitations of AI in medicinal chemistry. The Director will operate in a unique hybrid model environment, contributing to pioneering efforts in AI-driven drug design and working collaboratively to close Design-Make-Test-Analyze (DMTA) cycles more rapidly than traditional discovery teams. With a focus on innovation, strategic oversight, and scientific rigor, this role is integral to driving Takeda’s mission to deliver better medicines to patients faster and with higher success rates.

Job Requirements

PhD degree or equivalent experience
10+ years of relevant industry experience in drug discovery
Strong expertise in medicinal chemistry and SAR
Proven experience with lead optimization and candidate nomination
Ability to evaluate and critique AI-generated proposals
Experience with managing CRO synthesis projects
Familiarity with ADME and multi-parameter optimization
Knowledge of protein-ligand structural biology
Excellent collaboration and communication skills
Ability to work in fast-paced, autonomous environments
Willingness to be based in Cambridge, MA with hybrid work arrangement

Job Qualifications

PhD in medicinal chemistry or organic chemistry
10+ years of drug discovery experience with a strong track record in lead optimization and candidate nomination or MS with 16+ years experience, or BS with 18+ years experience
Demonstrated expertise in SAR-driven design, ADME optimization, and multi-parameter optimization across multiple programs
Experience directing CRO-based chemistry programs including synthetic route planning
proven vendor oversight and delivery management
Working knowledge of AI/ML design tools
ability to critically interpret and challenge computational outputs
Familiarity with structure-based drug design
ability to read and apply protein-ligand structural data
Experience in one or more therapeutic areas across the Takeda Research portfolio preferred
Has operated in SWAT team, rapid-response, or focused asset team environments and knows the difference between productive urgency and firefighting - and thrives in the former

Job Duties

Design and optimize drug candidates using deep SAR intuition, multi-parameter optimization, and ADME-embedded design principles
Integrate AI/ML-generated design proposals with medicinal chemistry judgment - critically evaluating computational outputs, not simply accepting them
Partner closely with Computational Chemistry colleagues to close DMTA cycles rapidly
Provide strategic oversight of CRO synthesis programs: define compound lists, review synthetic routes, and ensure quality and delivery timelines
Embed selectivity, developability, and IP considerations into design decisions from the outset
Interpret structural biology data (X-ray, cryo-EM) in partnership with Structural Biology contacts to guide optimization
Stay current with competitive chemical matter and emerging chemistry approaches
proactively apply new methods
Contribute to AI/ML training datasets by ensuring experimental data is captured in standardized, decision-ready format