VP, Head of Drug Discovery
$230k - $328kRelay Therapeutics
The Opportunity: 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. Your Role: Scientific Leadership 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 Organizational Leadership 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 Innovation 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. Your Background: 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. Estimated Salary Range: [$230,000 - $328,000]The range provided above is based on what we believe to be a reasonable estimate for this job at the time of posting. Actual base salary will depend on a number of factors, including but not limited to, a candidate’s education, experience, skills and location.#JO1About Relay TherapeuticsRelay Therapeutics is a clinical-stage precision medicines company transforming the drug discovery process with the goal of bringing life-changing therapies to patients. Built on unparalleled insights into protein motion and how this dynamic behavior relates to protein function, we aim to effectively drug protein targets that have previously been intractable, with an initial focus on enhancing small molecule therapeutic discovery in targeted oncology. Our Dynamo platform integrates an array of leading-edge experimental and computational approaches to provide a differentiated understanding of protein structure and motion to drug these targets. We have built a world-class team of leading experts from each of these disciplines, and they are driven by a deep collaboration at every step of our drug discovery process. Our team is equal parts fearless and relentless, with a shared passion for working collaboratively in intellectually stimulating environments. If you’re excited by the challenge of putting protein motion at the heart of drug discovery and passionate about making a difference in the lives of patients, join us!
$230k - $328k
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