Job Title: Computational ADMET Research Advisor
Location: Indianapolis, Indiana, United States
Job Category: Research & Development
Job Type: Full Time, Regular
Job ID: R-78012
At Lilly, we unite caring with discovery to make life better for people around the world. As a global healthcare leader, we strive to deliver life-changing medicines to those in need, improve the understanding and management of diseases, and contribute to our communities through philanthropy and volunteerism. We are looking for individuals who are passionate about making a real impact.
We are seeking a Computational ADMET Research Advisor to join our established computational ADME/Toxinformatics (cADMET) group within the Drug Disposition organization. This multidisciplinary team focuses on utilizing cutting-edge in silico methods to extract, share, and apply ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) structure-property relationship (SPR) knowledge across program teams. The ideal candidate will leverage their expertise in computational modeling to guide the development and optimization of drug candidates, enhance experimental design, and improve decision-making processes.
If you are ready to shape the future of drug discovery using your expertise in computational ADMET modeling, apply today to join our team at Lilly.
Computational Solutions Development: Independently research and develop computational tools to design and guide in vitro and in vivo experiments. Focus on enhancing scientific insights and decision-making.
Data Analysis & Integration: Conduct detailed data analysis to understand the connectivity between in silico, in vitro, and in vivo ADME and Safety data. Collaborate with project teams to optimize understanding and guide experimental designs.
Cross-Functional Collaboration: Serve as a liaison across various teams, including ADME, Toxicology, PKPD, Discovery Chemistry, Computational Chemistry, Research IT, and Data Science. Ensure seamless integration of computational ADMET tools across the discovery research organization.
Optimized Experiment Design: Communicate computational perspectives to facilitate rational, assumption-guided experiments that optimize multi-property drug candidates for project success.
PK Prediction & Preclinical Development: Use physiologically based pharmacokinetics (PBPK) principles for preclinical and clinical PK prediction, helping drive projects toward successful proof of concept in clinical trials.
Education: PhD in Computational Chemistry, Cheminformatics, Computational Biology, or related field.
Experience: More than 2 years of industry experience in developing and applying cheminformatics tools.
Skills: Proficiency in scripting/programming in one or more of the following languages: Python, C/C++, R. Experience with data analysis and visualization of multidimensional scientific data.
Interdisciplinary Experience: Demonstrated ability to work collaboratively with interdisciplinary teams.
Machine Learning & Statistical Methodologies: Experience applying advanced machine learning, statistical methodologies (e.g., uncertainty quantification, QSAR), and large-scale data mining for modeling.
Knowledge of ADMET & Pharmacokinetics: Familiarity with ADMET principles and pharmacokinetics.
Software Proficiency: Working knowledge of web-based applications, databases, and automated workflow tools like Knime.
Organic Chemistry Knowledge: Solid understanding of organic/medicinal chemistry.
Communication Skills: Strong verbal and written communication abilities, particularly in explaining complex scientific concepts to non-expert stakeholders.
Lilly is an EEO/Affirmative Action Employer and does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other legally protected status. We are committed to helping individuals with disabilities engage in the workforce, ensuring equal opportunities.
Ready to take the next step in your career and contribute to advancing life-changing therapies? Apply today!
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