Senior Computational Biologist - (12-month FTC)

About Us

About The Role

Key Responsibilities

• Build and own the ADMET prediction pipeline — toxicity, metabolic stability, membrane permeability, solubility — adapted specifically for peptide and protein-based sequences
• Integrate molecular docking and binding affinity prediction into the scoring workflow (structure-based where PDB data is available, ligand-based where it is not)
• Implement drug-likeness scoring for large molecules — beyond Lipinski rules, incorporating peptide-specific parameters (proteolytic stability, BBB penetration where relevant, oral bioavailability flags)
• Develop selectivity and off-target flagging methods to identify candidates with clean selectivity profiles before wet lab synthesis
• Build the active learning feedback loop: wet lab assay results feed back into the scoring model, improving prioritisation with each program cycle
• Interpret and present computational results to pharma clients — translate complex output into clear, actionable lead packages
• Collaborate with the engineering team on pipeline infrastructure, MLOps, and cloud compute architecture
• Define and maintain scientific documentation and data provenance for IP protection

Minimum Qualifications

• Direct hands-on experience in peptide and/or protein therapeutic drug discovery — this is a hard requirement. A purely small molecule background will not be considered.
• PhD in computational chemistry, structural biology, cheminformatics, or closely related field
• Minimum 5 years of post-PhD industry experience in drug discovery
• Proficiency with at least one major computational chemistry platform: Schrodinger Suite, AutoDock Vina, GROMACS, AMBER, or equivalent
• Python proficiency — you will build pipelines, not just run tools

Preferred Qualifications

• Experience building ADMET models specifically for peptides or biologics — not just small molecules
• Familiarity with peptide-specific challenges: proteolytic stability, membrane permeability, immunogenicity prediction
• Experience with structure-based drug design using cryo-EM or X-ray crystallography data
• Published work in peptide computational design or structure-based drug discovery
• Experience presenting computational results to pharma clients or non-computational stakeholders
• Familiarity with AlphaFold 2/3 and how to use predicted structures in drug design workflows
• Experience building or contributing to active learning / ML feedback loops in drug discovery

How To Apply