Research Advisor, Computational Chemistry

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 around the world 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.

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 seeking a highly motivated computational chemist to join our team and apply physics‑based modeling and cheminformatics to the design of chemically modified oligonucleotide therapeutics. The Lilly Genetic Medicine (LGM) organization is an innovation‑focused group dedicated to identifying, developing, and applying cutting‑edge technologies to maximize patient benefit. Our Data Science and AI/ML team partners closely with medicinal chemists, biologists, and data scientists across disciplines, playing a central role in molecular design, study design, and data‑driven decision‑making to accelerate RNA drug discovery.

Oligonucleotide therapeutics—including siRNAs, ASOs, and splice‑switching oligonucleotides—occupy a unique chemical space between small molecules and biologics. Each position in a therapeutic oligonucleotide can carry distinct sugar, backbone, and base modifications, creating a vast combinatorial design space that is poorly served by conventional computational chemistry tools. This role will bridge molecular simulation, cheminformatics, and machine learning to generate actionable insights that guide the optimization of chemically modified oligonucleotides across Lilly’s RNA therapeutics portfolio.

In this role, you will apply and develop computational chemistry methods to understand how chemical modifications influence the structure, stability, target engagement, and pharmacological properties of therapeutic oligonucleotides. You will work closely with medicinal chemists, biologists, and data scientists to translate atomic‑level insights into design recommendations that advance portfolio programs and platform capabilities.

Key responsibilities include:

• Perform molecular dynamics simulations of chemically modified oligonucleotide duplexes and single‑stranded species to characterize the structural and thermodynamic consequences of sugar, backbone, and base modifications
• Apply free energy methods (FEP, thermodynamic integration, MM/PBSA, MM/GBSA) to predict modification‑dependent binding affinities, duplex stability, and protein–oligonucleotide interactions
• Develop and validate force field parameters for novel nucleotide analogs using quantum mechanical calculations, enabling rapid computational evaluation of new chemistries emerging from the medicinal chemistry team
• Build and apply cheminformatics descriptors and QSAR/QSPR models adapted for chemically modified oligonucleotides, moving beyond sequence‑only representations to capture the full chemical diversity of the modification space
• Collaborate with medicinal chemists and biologists to integrate computational predictions with experimental SAR data, contributing to the identification of optimal modification patterns for on‑target potency, selectivity, metabolic stability, and safety
• Contribute to reusable computational workflows, data assets, and modeling platforms that support cross‑program learning and integration with the team’s unified machine learning models
• Present findings to cross‑functional teams and contribute to scientific strategy discussions,…