Senior Computational Biologist - (12-month FTC)
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Key skills for this role
About the Role
VentureOne is building a quantum-inspired drug discovery startup and seeks a Senior Computational Biologist to own the computational filtering and scoring stack. You will build ADMET prediction pipelines, integrate molecular docking, and implement drug-likeness scoring for peptides.
Key Skills for This Role
Responsibilities
- Build and own the ADMET prediction pipeline for peptide and protein based sequences.
- Integrate molecular docking and binding affinity prediction into the scoring workflow.
- Implement drug likeness scoring for large molecules, including peptide specific parameters.
- Develop selectivity and off target flagging methods.
- Build the active learning feedback loop from wet lab assay results.
- Interpret and present computational results to pharma clients.
- Collaborate with the engineering team on pipeline infrastructure, MLOps, and cloud compute architecture.
- Define and maintain scientific documentation and data provenance for IP protection.
Requirements
- Direct hands on experience in peptide and/or protein therapeutic drug discovery.
- 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 to build pipelines.
Full Job Posting
About Us
- VentureOne is building a new quantum inspired drug discovery start up headquartered in the UAE.
- Our technology is physics based: it takes any protein structure and generates thousands of amino acid hit sequences against any target in minutes.
- We want to deliver validated leads ready for pre clinical in a few months.
- We are a small, senior founding team.
About The Role
- We are hiring a Senior Computational Biologist / Chemist to build and own the computational filtering and scoring stack.
- This is the layer that turns 1,000+ raw sequences into 20 50 prioritised, drug like lead candidates.
- You will define the scientific rigour of every client deliverable.
- This is a 12 month contract position.
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.
- Implement drug likeness scoring for large molecules — beyond Lipinski rules, incorporating peptide specific parameters.
- Develop selectivity and off target flagging methods.
- Build the active learning feedback loop: wet lab assay results feed back into the scoring model.
- Interpret and present computational results to pharma clients.
- 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.
- 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.
- 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.
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