Research Advisor, Computational Chemistry
Eli Lilly and Company
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San Diego, CA
0
0
0
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Not Applicable
Posted April 3, 2026
2 variants
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Responsibilities
In this computational chemistry role at Eli Lilly and Company, you'll develop and apply molecular modeling, molecular dynamics, and free energy methods to understand how chemical modifications affect the structure, stability, binding, and pharmacological properties of therapeutic oligonucleotides. You will create and validate force field parameters, build cheminformatics descriptors and QSAR/QSPR models tailored to modified oligonucleotides, and integrate computational predictions with experimental SAR data to guide optimal modification designs. You’ll also contribute reusable workflows and data assets, collaborate closely with cross‑functional teams, and present your findings to influence scientific strategy, publications, and intellectual property.
Commitments
Eli Lilly and Company provides an accommodation request process for candidates who need assistance submitting a resume, and this process is intended solely for application-related accommodation requests; other inquiries will not receive a response.
Responsibilities
- 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.
- 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, publications, and patent applications
Commitments
If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) 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.
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Requirements
- Demonstrated expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymers
- Experience with free energy calculation methods applied to biomolecular systems
- Proficiency in cheminformatics toolkits (RDKit, OpenEye, or equivalent) and/or commercial CADD platforms (Schrödinger, MOE)
- Strong programming skills in Python, with experience in scientific computing libraries
- Familiarity with machine learning and AI methods applied to molecular sciences, including experience with predictive modeling for molecular properties, chemical optimization, or structure–activity relationships
- Excellent written and oral communication skills with ability to present complex computational results to diverse scientific audiences including medicinal chemists and biologists
- Experience with high‑performance computing and/or cloud‑based simulation environments
- Demonstrated ability to work collaboratively in cross‑functional team environments
- Experience with force field parameterization for non‑standard nucleotide analogs, including QM‑derived charge fitting (RESP, AM1‑BCC) and torsion parameter development
- Familiarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of modified nucleotides and their impact on duplex stability and reactivity
- Understanding of how chemical modifications influence oligonucleotide secondary structure, folding, and conformational dynamics, including modification‑dependent effects on duplex geometry and protein recognition
- Experience with machine learning approaches for molecular property prediction, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer data
- Familiarity with molecular representations for modified oligonucleotides (HELM, extended SMILES, or similar macromolecular encoding schemes)
- Knowledge of oligonucleotide‑specific ADME properties, including nuclease‑mediated metabolism, plasma protein binding of phosphorothioate backbones, and endosomal escape
- Track record of peer‑reviewed publications demonstrating expertise in computational chemistry applied to nucleic acids or modified biopolymers
- Deep understanding of nucleic acid structure and chemistry, including familiarity with common therapeutic modifications (2’‑OMe, 2’‑F, 2’‑MOE, LNA/cET, phosphorothioate, GalNAc conjugates)
- Experience designing computational workflows that integrate with automated experimental platforms and high‑throughput screening
Preferred Skills
- Demonstrated expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymers
- Experience with free energy calculation methods applied to biomolecular systems
- Proficiency in cheminformatics toolkits (RDKit, OpenEye, or equivalent) and/or commercial CADD platforms (Schrödinger, MOE)
- Strong programming skills in Python, with experience in scientific computing libraries
- Familiarity with machine learning and AI methods applied to molecular sciences, including experience with predictive modeling for molecular properties, chemical optimization, or structure–activity relationships
- Excellent written and oral communication skills with ability to present complex computational results to diverse scientific audiences including medicinal chemists and biologists
- Experience with high‑performance computing and/or cloud‑based simulation environments
- Demonstrated ability to work collaboratively in cross‑functional team environments
- Experience with force field parameterization for non‑standard nucleotide analogs, including QM‑derived charge fitting (RESP, AM1‑BCC) and torsion parameter development
- Familiarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of modified nucleotides and their impact on duplex stability and reactivity
- Understanding of how chemical modifications influence oligonucleotide secondary structure, folding, and conformational dynamics, including modification‑dependent effects on duplex geometry and protein recognition
- Experience with machine learning approaches for molecular property prediction, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer data
- Familiarity with molecular representations for modified oligonucleotides (HELM, extended SMILES, or similar macromolecular encoding schemes)
- Knowledge of oligonucleotide‑specific ADME properties, including nuclease‑mediated metabolism, plasma protein binding of phosphorothioate backbones, and endosomal escape
- Track record of peer‑reviewed publications demonstrating expertise in computational chemistry applied to nucleic acids or modified biopolymers
- Deep understanding of nucleic acid structure and chemistry, including familiarity with common therapeutic modifications (2’‑OMe, 2’‑F, 2’‑MOE, LNA/cET, phosphorothioate, GalNAc conjugates)
- Experience designing computational workflows that integrate with automated experimental platforms and high‑throughput screening
- Proficiency in Rust or other systems‑level languages for performance‑critical scientific computing is a plus
- Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions.
Education
- (Not required) – PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a closely related field
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.
Responsibilities
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, publications, and patent applications
Basic Requirements
PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a closely related field
Additional Skills/Preferences
Demonstrated expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymers
Experience with free energy calculation methods applied to biomolecular systems
Proficiency in cheminformatics toolkits (RDKit, OpenEye, or equivalent) and/or commercial CADD platforms (Schrödinger, MOE)
Strong programming skills in Python, with experience in scientific computing libraries
Familiarity with machine learning and AI methods applied to molecular sciences, including experience with predictive modeling for molecular properties, chemical optimization, or structure–activity relationships
Excellent written and oral communication skills with ability to present complex computational results to diverse scientific audiences including medicinal chemists and biologists
Experience with high‑performance computing and/or cloud‑based simulation environments
Demonstrated ability to work collaboratively in cross‑functional team environments
Experience with force field parameterization for non‑standard nucleotide analogs, including QM‑derived charge fitting (RESP, AM1‑BCC) and torsion parameter development
Familiarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of modified nucleotides and their impact on duplex stability and reactivity
Understanding of how chemical modifications influence oligonucleotide secondary structure, folding, and conformational dynamics, including modification‑dependent effects on duplex geometry and protein recognition
Experience with machine learning approaches for molecular property prediction, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer data
Familiarity with molecular representations for modified oligonucleotides (HELM, extended SMILES, or similar macromolecular encoding schemes)
Knowledge of oligonucleotide‑specific ADME properties, including nuclease‑mediated metabolism, plasma protein binding of phosphorothioate backbones, and endosomal escape
Track record of peer‑reviewed publications demonstrating expertise in computational chemistry applied to nucleic acids or modified biopolymers
Deep understanding of nucleic acid structure and chemistry, including familiarity with common therapeutic modifications (2’‑OMe, 2’‑F, 2’‑MOE, LNA/cET, phosphorothioate, GalNAc conjugates)
Experience designing computational workflows that integrate with automated experimental platforms and high‑throughput screening
Proficiency in Rust or other systems‑level languages for performance‑critical scientific computing is a plus
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 (https://careers.lilly.com/us/en/workplace-accommodation) 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.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
$166,500 - $266,200
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
#WeAreLilly
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.
Responsibilities
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, publications, and patent applications
Basic Requirements
PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a closely related field
Additional Skills/Preferences
Demonstrated expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymers
Experience with free energy calculation methods applied to biomolecular systems
Proficiency in cheminformatics toolkits (RDKit, OpenEye, or equivalent) and/or commercial CADD platforms (Schrödinger, MOE)
Strong programming skills in Python, with experience in scientific computing libraries
Familiarity with machine learning and AI methods applied to molecular sciences, including experience with predictive modeling for molecular properties, chemical optimization, or structure–activity relationships
Excellent written and oral communication skills with ability to present complex computational results to diverse scientific audiences including medicinal chemists and biologists
Experience with high‑performance computing and/or cloud‑based simulation environments
Demonstrated ability to work collaboratively in cross‑functional team environments
Experience with force field parameterization for non‑standard nucleotide analogs, including QM‑derived charge fitting (RESP, AM1‑BCC) and torsion parameter development
Familiarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of modified nucleotides and their impact on duplex stability and reactivity
Understanding of how chemical modifications influence oligonucleotide secondary structure, folding, and conformational dynamics, including modification‑dependent effects on duplex geometry and protein recognition
Experience with machine learning approaches for molecular property prediction, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer data
Familiarity with molecular representations for modified oligonucleotides (HELM, extended SMILES, or similar macromolecular encoding schemes)
Knowledge of oligonucleotide‑specific ADME properties, including nuclease‑mediated metabolism, plasma protein binding of phosphorothioate backbones, and endosomal escape
Track record of peer‑reviewed publications demonstrating expertise in computational chemistry applied to nucleic acids or modified biopolymers
Deep understanding of nucleic acid structure and chemistry, including familiarity with common therapeutic modifications (2’‑OMe, 2’‑F, 2’‑MOE, LNA/cET, phosphorothioate, GalNAc conjugates)
Experience designing computational workflows that integrate with automated experimental platforms and high‑throughput screening
Proficiency in Rust or other systems‑level languages for performance‑critical scientific computing is a plus
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 (https://careers.lilly.com/us/en/workplace-accommodation) 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.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
$166,500 - $266,200
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
#WeAreLilly