Senior Scientist, Computational Chemistry

Iambic Iambic · Pharma · San Diego, CA · Science

Seeking a Computational Chemist with 5+ years of industry drug discovery experience to apply ligand and structure-based techniques, collaborate with ML scientists, and integrate computational methods with proprietary AI technologies for drug discovery. The role involves guiding design cycles, evaluating virtual libraries, and developing computational workflows.

What you'd actually do

  1. Apply a range of ligand and structure-based approaches across hit identification, hit to lead and lead optimization phases (e.g. cheminformatics, pharmacophore modeling, docking and shape-based based virtual screening, de novo molecular design, MD simulation)
  2. Guide design cycles through the identification of structure-activity relationships and the integration of computational predictions with bioactivity data
  3. Partner with medicinal chemists to design, evaluate and prioritize virtual libraries, and propose actionable molecular designs
  4. Partner with ML scientists to integrate SBDD methods with our proprietary transformer-based property prediction technology (Enchant)
  5. Leverage and evolve our proprietary protein-ligand co-folding technology (NeuralPLexer) to yield novel and actionable structural hypotheses

Skills

Required

  • Computational chemistry
  • Ligand-based design
  • Structure-based design
  • Python programming

Nice to have

  • MOE molecular modeling package
  • MD simulation
  • MM/GBSA
  • ML-augmented small molecule discovery
  • Protein-ligand co-folding

What the JD emphasized

  • PhD or equivalent degree in computational chemistry (or related field) with 5+ years of experience working in an industry drug discovery environment
  • Experience supporting active small molecule drug discovery programs
  • Experience with ligand and structure based design tools
  • Python programming
  • Experience with ML-augmented small molecule discovery
  • Experience in the application of co-folding for small molecule discovery

Other signals

  • application of advanced ligand and structure-based techniques to drive drug discovery efforts
  • collaborating closely with machine learning scientists
  • integrate SBDD methods with our proprietary transformer-based property prediction technology
  • Leverage and evolve our proprietary protein-ligand co-folding technology