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Cheminformatician na Variational AI

Remoto - Vancouver, BC (OR Remote)

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Variational AI is radically accelerating the development of promising drug candidates by integrating chemistry and pharmacology expertise with the state-of-the-art in machine learning. Traditional approaches to small molecule drug discovery require over ten years and two billion dollars, and their reliance on trial-and-error calls out for better predictive and generative models. The current industry standard has progressed little beyond shallow ML techniques such as random forests and support vector machines, largely due to the difficulty of integrating world-class machine learning research with traditional chemistry and pharmacology approaches. Variational AI is building a generative foundation model for molecular structure and properties from the ground up. Over the past five years, we have been advancing the state-of-the-art, and delivering projects to customers including Merck, Rakovina Therapeutics, and ImmVue Therapeutics. We are searching for a cheminformatician to join us in our quest to radically accelerate the development of new drugs through machine learning excellence. You will help identify, clean, prepare, and test datasets; develop ligand- and structure-based featurizations; apply traditional cheminformatics techniques; and evaluate new targets. In this process, you will have the opportunity to build your skills by collaborating with our team of accomplished ML scientists, computational chemists, and medicinal chemists. No knowledge of machine learning is required for this role, but you should possess a strong interest in learning about this promising technology, coupled with hands-on drug discovery experience.

Requirements

Education

  • M.S. in chemistry
  • Ph.D. in chemistry (preferred)

Experience

  • 2+ years in small molecule drug discovery

Skills

  • Python programming
  • cheminformatics
  • data analysis
  • RDKit
  • Matplotlib
  • pandas
  • seaborn
  • SQL
  • QSAR
  • molecular docking
  • molecular dynamics
  • ABFE
  • RBFE
  • ADMET modeling

Responsibilities

  • Identify, clean, prepare, and test datasets
  • Develop ligand- and structure-based featurizations
  • Apply traditional cheminformatics techniques
  • Evaluate new targets

Technologies

PythonRDKitMatplotlibpandasseabornSQLQSARmolecular dockingmolecular dynamicsABFERBFE

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