Computational Biologist / Bioinformatician

Who we are

Peptris is an AI-first preclinical drug discovery company. We apply AI and computational tools to the challenge of designing new molecules for validated targets — bringing together biology, chemistry, and AL/ML under one roof. Our team is small, driven by curiosity, and believes the best drug discovery ideas come from people who can think across disciplines.

What this role is about

You will bring biological depth to a team that works at the intersection of biology, chemistry, AI, and data. Your job is to understand biology well enough to ask better computational questions — and to help ensure our AI models are solving real, meaningful problems. You don't need to be a coder first and a biologist second; we need someone who is deeply both.

What you'll do

• Work with protein structures — interpret protein structure data, assist with binding pocket analysis, and map key residues for drug interaction
• Integrate and analyse data from biological databases: PDB, UniProt, ChEMBL, Open Targets, STRING, and others
• Collaborate with AI/ML engineers to frame biological problems as computable tasks and validate model outputs against biological reasoning
• Build data pipelines and automation scripts for biological annotation and structure-activity analysis in Python
• Support structure-based and ligand-based drug design workflows — molecular docking, pharmacophore modelling, and virtual screening
• Stay current with scientific literature and bring relevant findings into active team discussions
• Touch on target biology, pathway context, and disease relevance where needed — but your core focus is molecules and structures, not omics pipelines

What you bring

• Bachelor’s in Bioinformatics, Biotechnology, Biochemistry, or Life Sciences + MSc/MTech or PhD in Bioinformatics, Computational Biology, Computational Chemistry, or a related field
• Strong programming skills in Python — data wrangling, statistical analysis, visualisation
• Hands-on experience with structural biology tools: PyMOL, AutoDock, GROMACS, HADDOCK, or similar
• Familiarity with sequence and structure analysis tools: BLAST, HMMER, ClustalW, or equivalent
• Working knowledge of key databases: PDB, UniProt, ChEMBL, Open Targets, Reactome, KEGG
• Comfort with Linux/bash, Jupyter notebooks, Git, and reproducible research practices
• An instinct for biological plausibility — you can tell when a computational result doesn't make biological sense
• Interest in ML/AI and genuine curiosity about applying it to drug discovery problems

What we actually care about

• You understand biology deeply and can explain it clearly to non-biologists
• You're genuinely excited about what AI can do for drug discovery — not just in theory
• You're comfortable running an analysis you've never done before, guided by a paper and a hypothesis
• You see computational tools as creative instruments, not just pipelines to run
• You bring the team closer to the biology every time you open your mouth
• You can sit between a chemist and an ML engineer and help both of them

#Bioinformatics #ComputationalBiology #ComputationalChemistry #DrugDiscovery #StructuralBiology #MolecularDocking #BindingPocketAnalysis #AlphaFold #PyMOL #GROMACS #AutoDock #RDKit #ChEMBL #PDB #UniProt #OpenTargets #VirtualScreening #LigandBasedDesign #StructureBasedDesign #AIinDrugDiscovery #BioAI #Python #Pharmacophore #MolecularDesign #Peptris #Hiring