Senior Principal Scientist, Computational Drug Discovery – Molecular Modeling & Cheminformatics

About Parabilis Medicines Parabilis Medicines is a clinical-stage biopharmaceutical company dedicated to creating extraordinary medicines for patients with serious diseases by unlocking biologically important targets long considered undruggable. The company has pioneered a new class of alpha-helical peptides – Helicons™ – capable of modulating intracellular proteins that have historically been beyond the reach of conventional medicines. The company’s lead investigational medicine, zolucatetide, is the first and only direct inhibitor of the β-catenin:TCF interaction, a central node in the Wnt/β-catenin pathway that has eluded drug developers for decades. Zolucatetide is being evaluated in the clinic across multiple Wnt/β-catenin-driven diseases, including desmoid tumors, familial adenomatous polyposis (FAP) and a range of other solid tumor indications. Beyond zolucatetide, Parabilis is advancing additional Helicon-based programs focused on other challenging targets where we believe our medicines could have life-altering impact. For more information, visit www.parabilismed.com or follow us on LinkedIn . What’s the opportunity? Parabilis Inc. is seeking a highly talented and self-motivated Senior Principal Scientist to be a versatile, hands-on pillar of the Computational Drug Discovery group, a strategic function that is part of Parabilis’s platform discovery engine for Helicon™ stapled-peptide drugs. Reporting to the Senior Director of Computational Drug Discovery, this person will bridge two disciplines that are often siloed — 3D molecular modeling and molecular data science / cheminformatics — applying both to advance our peptide pipeline. We are looking for someone who can independently support project teams across the full computational spectrum, from sampling peptide conformations and designing ternary complexes to building predictive property models and running cheminformatics analyses, without needing to hand off between a dedicated 3D modeler and a dedicated cheminformatician. You’ll be part of a data science team that is a central pillar of Parabilis’s innovative discovery platform and pipelines targeting “undruggable” genes of major therapeutic interest to patients. Key responsibilities: Provide computational expertise toward, but not limited to, ternary complex design for degraders and other proximity-based modalities, hit identification and prioritization, hit-to-lead progression using multi-objective optimization, initiating new projects, new drug-target assessments, and advancing drug-pipeline projects toward the clinic. Identify, implement, and apply 3D modeling techniques for sampling Helicon™ peptide conformations in the presence of a target, in ternary complex, and in different physiological environments; analyze and derive 3D peptide structure-property relationships. Analyze peptide/chemical structure and property space to identify patterns and gaps that inform Helicon and monomer designs; adapt and implement data analytics and machine learning techniques toward predictive models of Helicon™ properties. Contribute to the implementation and development of cheminformatics and research informatics systems and tools, working with IT and data engineering to enable and automate enterprise-level computational solutions. Exemplify scientific leadership by partnering across functions and working within a team of talented, passionate scientists to discover drugs and help teams make better decisions, faster. Interface with internal and external partners. What you’ll need to be successful: PhD in Computational Chemistry, Cheminformatics, Data Science, Protein Engineering, Computational Biology/Biophysics, Chemistry, Physics, Macromolecular Sciences, or a closely related field. 10+ years of pharma/biotech industry experience in computational rational drug discovery, with a proven track record of impact in a drug discovery program using computational and/or informatics techniques. Demonstrated experience with ternary comple