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

Parabilis Medicines•Cambridge, MA

4d•$200,000 - $250,000•Onsite

About The Position

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.

Requirements

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 complex design and understanding, peptide design or protein engineering, and a good understanding of peptide structure-property relationships (e.g. helicity and amphiphilicity metrics, cell penetration).
Demonstrated mastery of modern computational chemistry including, but not limited to, peptide folding and docking, use of co-folding foundation models such as Boltz, structure-based design (receptor- and ligand-based), scaffold hopping, and conformational analysis.
Demonstrated expertise with cheminformatics (e.g. RDKit, ICM, OEChem) and data science toolkits/libraries (e.g. Pandas, Scikit-Learn, NumPy), with a solid grasp of statistical principles and data analytics.
Expertise in one or more peptide modeling environments (e.g. ICM, Rosetta, Amber, GROMACS) and methods (enhanced sampling MD, Monte Carlo, MSM).
An understanding of modern drug discovery including, but not limited to, medicinal chemistry, multi-parametric optimization, and molecular recognition principles, and the ability to adapt and translate these principles to stapled peptides.
Demonstrated understanding of critical assessment of molecule-property data and predictive model quality, and of the experiments behind the data that can be translated to computational analysis.
Strong scientific programming skills (Python) in a Linux environment, and experience with command-line modeling applications.
Experience with data visualization and exploration tools (e.g. Vortex, Spotfire, DataWarrior, Datagrok).
Excellent communication and collaboration skills, with the ability to work well in and inspire a vibrant, multidisciplinary community of drug hunters.
Excellent organizational skills and attention to detail, with a strong passion for learning new concepts and technologies.
Demonstrated use of AI tools in your current role and responsibilities is required.

Nice To Haves

Familiarity with cloud computing environments (e.g. Google, AWS, Azure) is a plus.
Experience with enterprise research informatics systems such as Dotmatics and chemical and biological data warehouses is a plus.
Familiarity with machine learning and cheminformatics concepts applied to peptides is a plus.
Familiarity with use of LLMs and IDEs, such as Cursor, to enhance workflows, productivity and innovation is a plus.

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.