Applying generative machine learning algorithms to generate novel small molecules for drug discovery- BC-590

Desired discipline(s): Engineering - biomedical, Engineering, Engineering - chemical / biological, Engineering - computer / electrical, Biochemistry / Molecular biology, Life Sciences, Pharmacy / Pharmacology, Computer science, Mathematical Sciences
Company: Variational AI inc
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Canada
No. of positions: 1
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About the company: 

Founded in September 2019, we are a team of experienced AI/machine learning and business specialists applying state-of-the-art generative AI to drug discovery in close collaboration with biopharmaceutical partners to bring new therapeutics to market across disease areas and positively impact lives.

Please describe the project.: 

We are seeking a computational chemistry graduate student to join Variational AI to work on a project to apply state-of-the-art generative machine learning algorithms to generate novel small molecules that are efficacious, safe, and synthesizable. The student would join a team of leading machine learning researchers with experience from companies such as D-Wave Systems, Google, and Microsoft working on diverse targets across disease areas. The primary role of the student would be to work on generating docking scores to help train our generative AI, though additional scope is available based on the candidate's education, experience, and interests. This opportunity is ideally suited for a graduate student who has interest in AI/machine learning but no experience. The student will work alongside some of the leading machine learning researchers working in drug discovery.

Required expertise/skills: 

  • Molecular docking experience
  • Basic Shell scripting
  • Some background in Chemistry/Biochemistry
  • Preferable to have medicinal chemistry experience
  • Preferable to have some basic python scripting experience
  • Knowledge/experience in machine learning is not necessary