Science and health

Drug discovery and molecular design

Drug discovery and molecular design uses AI to decide which biological targets, compounds and experiments are worth the lab's time. AI companies need it to turn model outputs into candidates that scientists can make, test and trust.

Models can now propose far more molecules and protein sequences than any lab can test, so someone has to choose well. Hiring is tricky because the skills sit across disciplines. A strong candidate respects that predictions need experimental validation and do not establish clinical efficacy. Look for people who can name what a model got wrong, and what the assay showed.

Hiring now 2Emerging 2

Roles in this family

4 roles

Hiring nowAI Drug Discovery ScientistAn AI Drug Discovery Scientist uses predictive models and experimental evidence to decide which targets and compounds are worth testing. They help…Hiring nowProtein Design ScientistA Protein Design Scientist designs protein sequences and structures that meet defined functional targets, then plans the experiments to prove they…EmergingMolecular Generative Modelling ScientistA molecular generative modelling scientist builds models that propose new, chemically valid compounds under clear constraints. They give a drug…EmergingAI Medicinal Chemistry SpecialistAn AI Medicinal Chemistry Specialist weighs model-generated compound suggestions against real chemistry judgement and decides what gets made and…

Where the people come from

Computational chemistry, structural biology, pharmacology or ML

Not to be confused with. Predictions require experimental validation and do not establish clinical efficacy.

Core skills

  • Experimental design
  • Scientific domain expertise
  • Biomolecular modelling
  • Lab automation and biosafety

Where it is used

  • Drug discovery and biotechnology
  • Chemistry and materials

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