Intern - Computational Chemistry - Remote

Overview

The internship project will explore de novo molecular design using generative AI models and have a focus on evaluating existing public domain implementations of algorithms for synthetic pathway generation.

In Short

• 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.
• Run generative methods on protein targets of interests.
• Evaluate the quality of the results with an emphasis on synthetic accessibility.
• Prepare and present results at an internal Arvinas function.

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.
• 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.
• Must be able to work effectively in both independent and team contexts.
• Arvinas will not be providing VISA sponsorship for this position.

Benefits

• Competitive package of base and incentive compensation.
• Comprehensive benefits program designed to support health and wellness.
• Group medical, vision and dental coverage.
• Group and supplemental life insurance.