Summer Intern - Computational Chemistry & Artificial Intelligence

Summary

Join Arvinas, a clinical-stage biotechnology company, as an intern to explore de novo molecular design using generative AI models. This internship focuses on evaluating public domain algorithms for synthetic pathway generation. You will collaborate with the CADD team, configuring, running, and deploying generative models, comparing results to assess synthetic tractability. The internship culminates in a presentation of your findings. This remote position reports to a Research Investigator in Computational Chemistry and requires proficiency in Python and cheminformatics toolkits. Arvinas does not offer visa sponsorship.

Requirements

• A knowledge of organic chemistry at the undergraduate level or higher is required
• Proficiency in Python programming required
• Experience with a cheminformatics toolkit (RDKit, Openeye, Schrodinger, etc.) is required
• Experience using version control (GitHub, GitLab, Bitbucket, etc.) required
• Must be able to work effectively in both independent and team contexts
• Arvinas will not be providing VISA sponsorship for this position. You must have the ability to work without a need for a current or future VISA sponsorship
• Computational Chemistry/Biology, Chemistry, Biology, or Computer Science major

Responsibilities

• Explore generative AI approaches for de novo drug design and synthetic pathway generation
• Dive deep into molecular datasets, creating statistically rigorous training, validation, and test sets to optimally retrain generative AI models for real-world prediction and generation tasks
• Run generative methods on protein targets of interests and evaluate the quality of the results with an emphasis on synthetic accessibility of the designed molecules
• Prepare and present results at an internal Arvinas function at the conclusion of the internship

Preferred Qualifications

• Experience working on medicinal chemistry projects is desirable
• Knowledge of and experience with at least one ML framework (TensorFlow, PyTorch or Keras) is desirable
• Knowledge of RNNs, LSTMs, Transformers and LLMs is desirable
• Completion of classes in Computational Chemistry, Chemistry, and/or Molecular Biology & Biophysics strongly preferred
• Preference for doctoral graduate students nearing degree completion

Benefits

• Competitive package of base and incentive compensation
• Comprehensive benefits program designed to support the health, wellness and financial security of our employees and their families
• Group medical, vision and dental coverage
• Group and supplemental life insurance