Schrödinger seeks a Retrosynthesis Researcher in Machine Learning (ML) to join us in our mission to transform the discovery of therapeutics and materials.

Schrödinger has pioneered a physics-based software platform that enables discovery of high-quality novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is used by biopharmaceutical and industrial companies academic institutions and government laboratories around the world. Our multidisciplinary drug discovery team also leverages the software platform to advance collaborative programs and its own pipeline of novel therapeutics to address unmet medical needs.

• An ML expert who has applied AI tools to chemical reaction prediction or retrosynthesis (e.g. reaction templates template-free approaches) and understands organic synthesis and reaction mechanisms
• An experienced user of cheminformatics tools (e.g. RDKit Open Babel)
• A proficient Python programmer who’s familiar with ML tools like Pytorch Tensorflow and JAX
• An excellent problem-solver who’s comfortable working collaboratively in a multidisciplinary research environment

What you’ll do:

• Develop and implement AI/ML models (e.g. graph neural networks transformer-based models) for retrosynthetic pathway prediction
• Apply deep learning techniques to predict reaction outcomes optimize reaction conditions and identify novel synthetic routes
• Curate and manage reaction datasets from literature patents and proprietary sources to train and validate predictive models
• Integrate retrosynthesis tools with cheminformatics platforms and molecular modeling software
• Collaborate with synthetic chemists to experimentally validate predicted retrosynthetic routes and optimize laboratory workflows
• Contribute to scholarly publications in high-impact journals and represent the research group in conferences and workshops

What you should have:

• PhD in Chemistry Computational Chemistry Cheminformatics or a related field
• A solid publication record that demonstrates expertise in retrosynthesis algorithms and computational chemistry

We’d prefer to hire someone who has:

• Familiarity with chemical reaction databases (e.g. Reaxys USPTO Pistachio)
• Knowledge of computer-aided synthesis planning (CASP) tools and retrosynthetic analysis software (e.g. AiZynthFinder ASKCOS IBM RXN)
• A background in graph-based learning attention mechanisms and transformer architectures applied to chemical data
• Familiarity with reaction condition prediction and reaction yield optimization.
• Experience with Schrödinger Suite and LiveDesign
• Experience with de novo design and generative machine learning methods
• Experience with cloud computing and/or high-performance computing (HPC) resources
• Exposure to quantum chemistry (DFT) is a plus