VP, Head of Drug Discovery

Relay Therapeutics•Cambridge, MA

About The Position

We are seeking an exceptional scientific leader to oversee our integrated Medicinal Chemistry, Computational Chemistry, Cheminformatics, and Drug Metabolism & Pharmacokinetics (DMPK) organizations. This individual will be responsible for driving the discovery and optimization of transformative small molecule therapeutics by building a highly collaborative, data-driven discovery engine that combines deep scientific expertise with modern computational approaches. The successful candidate will be a recognized leader in small molecule drug discovery with a proven track record of advancing programs from hit identification through IND filing. They will be a key member of research leadership, fostering an environment where all disciplines work as a unified team to rapidly test hypotheses, learn from experimental data, and deliver differentiated medicines. This role is ideal for a leader who is energized by solving complex scientific problems, embracing emerging technologies, and building high-performing scientific organizations within a fast-paced biotechnology environment. We believe breakthrough medicines emerge from the seamless integration of human creativity, rigorous experimentation, and advanced computational science. Our discovery strategy combines hypothesis-driven biology, medicinal chemistry, computational chemistry, structural biology, and DMPK to iteratively design, test, and refine molecules based on quantitative experimental evidence. By coupling deep scientific expertise with state-of-the-art computational technologies, we aim to accelerate learning, improve decision quality, and deliver differentiated therapies for patients with serious diseases.

Requirements

Ph.D. in Organic Chemistry or Computational Chemistry.
15+ years of progressively increasing leadership responsibility in biotechnology and/or pharmaceutical drug discovery.
Demonstrated success leading multidisciplinary discovery organizations.
Proven track record of advancing multiple small molecule programs from target identification through development candidate nomination.
Deep understanding of medicinal chemistry, molecular design, ADME/DMPK, structure-based drug design, and computational chemistry.
Demonstrated success implementing modern computational approaches including AI/ML applications, molecular modeling, predictive property models, and data-driven discovery workflows into medicinal chemistry decision making.
Experience with emerging modalities including heterobifunctional and other beyond rule of 5 approaches.
Strong scientific publication record and recognition within the drug discovery community.
Excellent communication and organizational leadership skills.

Responsibilities

Lead and integrate the Medicinal Chemistry, Computational Chemistry, Cheminformatics, and DMPK organizations to create a highly effective, collaborative drug discovery engine.
Define and execute scientific strategies that enable rapid progression from target validation to development candidate nomination.
Champion hypothesis-driven drug discovery by integrating predictive computational methods and medicinal chemistry.
Ensure that molecular design decisions are informed by a comprehensive understanding of potency, selectivity, physicochemical properties, ADME, safety, developability, and translational considerations.
Drive scientific excellence through rigorous data analysis, critical thinking, and evidence-based decision making.
Ensure efficiency and optimal resource utilization through integration of emerging technologies when appropriate.
Build strong partnerships across biology, biomolecular sciences, translational medicine, and project leadership.
Guide portfolio prioritization and resource allocation.
Create an environment where multidisciplinary teams operate with shared objectives and transparent communication.
Remove organizational barriers that limit collaboration and ensure efficient decision-making across discovery functions.
Work across functions to ensure successful project transitions into development.
Drive adoption of technologies including AI/ML-enabled molecular design, structure-based drug design, molecular simulations, free-energy calculations, generative chemistry, predictive ADME models, cheminformatics, and high throughput experimentation.
Promote rigorous evaluation of emerging technologies while maintaining strong emphasis on experimental validation.
Ensure computational methods accelerate discovery through measurable scientific impact rather than technology adoption alone.