CADD Postdoctoral Scientist

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees work to discover and bring life‑changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first.

We’re looking for people who are determined to make life better for people around the world. #We Are Lilly

Eli Lilly’s EMD C3 (Computational Chemistry & Cheminformatics) group is seeking a Postdoctoral Scientist to advance Synth(on)-Fragment–based Virtual Screening (SFBVS) for early small‑molecule drug discovery. This role focuses on developing synthesis‑aware virtual screening workflows that connect fragment‑level binding hypotheses to synthesizable chemical space
, lowering barriers to synthesis and accelerating hit discovery. The position offers a unique opportunity to work at the intersection of AI, structure‑based drug design, fragment chemistry, and synthesis
, contributing directly to Lilly’s next‑generation virtual screening capability.

• Develop and apply synth(on)-fragment–based virtual screening methods for small‑molecule hit discovery.
• Design fragment growth, linking, and merging strategies constrained by real‑world synthetic feasibility.
• Integrate structure‑based modeling (docking, scoring, ML/DL affinity prediction) with reaction‑aware generative designs.
• Prioritize compounds that balance binding potential, chemical diversity, and synthetic accessibility.
• Collaborate closely with medicinal chemists, fragment scientists, and screening teams to enable rapid experimental validation.
• Apply workflows to novel and challenging EMD targets, including difficult or under explored pockets.

• PhD in Computational Chemistry, Computer Science, or a related field.
• Strong background in CADD and structure‑based drug design.
• Experience with fragment‑based drug discovery concepts.
• Proficiency in Python and familiarity with cheminformatics tools (e.g., RDKit).
• Proficiency in DL/ML methods for binding prediction or molecular design.
• Ability to work with large chemical libraries and computational workflows.
• Qualified applicants must be authorized to work in the United States on a full‑time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.

• Experience with synth(on)- or reaction‑based chemical libraries.
• Familiarity with synthetic accessibility scoring, retrosynthesis, or reaction rule systems.
• Experience collaborating with medicinal chemistry teams.
• Strong collaboration and scientific communication skills.

• Contribute directly to high‑priority small‑molecule discovery programs.
• Help define scalable, synthesis‑aware virtual screening strategies.
• Work in a highly collaborative, multidisciplinary AI‑driven environment.
• Access industry‑leading compute, data, and experimental platforms.

• The position is located ovation Center, Boston.
• Onsite working model.
• This position is not permanent. It is a fixed‑term position up to a maximum of 4 years.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form () for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Actu…