Advisor of Computational Peptide Biochemistry

Responsibilities

• Contribute as the lead biochemist and assay biologist for the team on macrocyclic discovery, contributing both insight and developing strategy for assays for various efforts in the group.
• Work on (both directionally and operationally) molecule testing efforts from lead identification to lead optimization and contribute along the way to phase 1b and beyond.
• Provide assay development expertise, and support for macrocyclic peptide discovery projects.
• Develop methodologies, protocols and run SPR and other protein binding assays for characterization of compounds.
• Characterize complete functionality of protein reagents, provide both insight and oversight into quality and tracking of protein reagents, be able to independently lead a small team of biochemists and biologists to support a large molecule discovery group with various targets and assets at different stages of discovery.
• Work collaboratively in the group to achieve a common goal but also provide independent perspective and scientific rationale and contribute to strategy and execution.
• Take on dedicated efforts towards targets of high conviction and utilize speed and prioritization to advance assay efforts both at Protomer and wider Lilly groups and through collaboration with the rest of research organization.
• Inspire people to collaborate in inventing great medicines by removing barriers, committing to high quality scientific hypotheses, and accelerating where possible.
• Keep safety as a top priority at all times, striving toward a proactive safety culture.
• Be a good team player and work effectively, responsibly and professionally with colleagues at Protomer and across Lilly.

Qualifications

• PhD in computational chemistry, medicinal chemistry, computational biology, machine learning, cheminformatics, biophysics, or closely related discipline.
• 0-3 years of drug discovery experience with demonstrated track record of computational impact on peptide discovery programs in academia, biotech, or pharma.
• Strong track record in AI/ML, computational tools and molecular designs for linear and cyclic peptides and proactively apply new approaches to develop peptide drug design tools.
• Experience integrating generative AI and/or LLM, structure-based design, free energy perturbation to peptide SAR studies from hit identification to lead optimization in peptide drug discovery programs.
• Proficiency across computational tools in molecular docking, molecular dynamics simulation, FEP/free-energy methods, QSAR/ADMET modelling.
• Experience with virtual screening campaigns, structure-based peptide drug design and ability to interpret crystallography or cryo-EM data into implement in the drug design tools computational design strategies.
• Strong programming, preferably Python, with experience using cheminformatics toolkits such as RDKit and modern data science workflows.
• Familiar with cloud-based or high-performance computing environments.
• Familiar with peptide screening technologies (mRNA display, one-bead-one-compound libraries, yeast display, phage display, etc) to accelerate design-make-test-analyse cycle of peptide discovery programs.
• Familiar with peptide medicinal chemistry principles, physicochemical property optimization of peptide as modalities, oral macrocyclic peptide properties, ADME/PK concepts.
• Partner closely with peptide discovery scientists, peptide chemists and medicinal chemists to translate computational data into hit identification/lead optimization strategies.