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.

The ideal candidate will join an established computational ADME/Toxinformatics (c ADMET) group within the multidisciplinary Drug Disposition organization. This group focuses on using groundbreaking in silico approaches to facilitate the extraction, sharing, and application of ADMET structure-property relationship (SPR) knowledge across program teams. The role involves actively engaging with various team members to identify issues and design innovative experiments that enhance the chances of achieving SAR improvements for successful clinical candidates. The candidate will maintain the existing in silico ADMET toolbox and innovate and implement new techniques and models as needed.

Job Responsibilities

• Independently research, develop, and deliver computational solutions. Design and guide in vitro and in vivo experiments. Focus on maximizing scientific insights and decision-making knowledge.
• Conduct concerted data analysis to understand the connectivity across in silico, in vitro, and in vivo ADME and Safety data for project teams.
• Serve as a liaison to colleagues in ADME, Toxicology, PKPD, Discovery Chemistry, Computational Chemistry, Research IT, and Data Science for seamless integration of computational ADMET tools across the discovery research organization.
• Use physiologically based pharmacokinetics (PBPK) principles for preclinical and clinical PK prediction to effectively drive projects toward successful proof of concept in the clinic.

Basic Requirements

• Ph D in computational chemistry, cheminformatics, computational biology or a related field with experiences in developing and applying cheminformatics tools for application in drug discovery

Additional Preferences

• More than 2 years of industry experience in developing and applying cheminformatics tools
• Demonstrated results/experience in working with interdisciplinary teams.
• Experience in applying advanced machine learning and statistical methodologies including QSAR for large scale data mining and modeling
• Proficient in scripting/programming in one or more of the following languages: Python, C/C++, R
• Working knowledge of principles of ADMET and pharmacokinetics
• Proficiency in analyzing and visualizing multi-dimensional scientific data
• Proactive learning ability and independent problem-solving skills
• Solid understanding of organic/medicinal chemistry
• Familiarity in working with web-based applications, databases and automated workflow tools such as Knime
• Strong communication skills

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