Calling all

Chemical Space Explorers

Join us in exploring unchartered regions of chemical space to discover first-in-class medicines.

At DeepCure, we are looking for individuals with out-of-the-box thinking. We’re working on some of the world’s most challenging problems, with no existing solutions, so we need the most innovative, talented and passionate scientists, engineers and ML researchers.

Our values and culture

  • We are building the biopharma company of the future using an innovative, startup culture.
  • Innovation in every aspect of our team – from drug development, culture to biology.
  • Problem-solvers with extremely diverse backgrounds.
  • Impact-driven – shared goal of helping bring innovative new small molecule drugs to patients, faster.
  • Curious – actively seeking opportunities to learn and improve. Staying on top of our game and always seeking alternative perspectives. 

Join us in exploring unchartered regions of chemical space to discover first-in-class medicines.

If you would like to be considered for a position at Deepcure, please submit your resume and cover letter to

Molecular Foundry

At DeepCure, automation isn’t just about reducing the cost and time of compound synthesis – it’s about going beyond the limits of manual synthesis to carry innovation all the way through the design-build-test-learn cycle. Our foundry unlocks the chemical space that AI drug design tools want to explore but can’t because it is not practicably available to most chemists. DeepCure’s Molecular Foundry is built to expand the usable chemical space for drug discovery through increased synthesis success rates, removal of human bias in synthesis, and greater efficiency of custom multi-step synthesis.

Automated Synthesis

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Our patent-pending molecular generation tool, MolGen™, designs novel, diverse compounds. Using state-of-the-art deep reinforcement learning (RL), MolGen™ constructs synthesizable compounds with features that capture the important molecular interactions for binding and selectivity, as well as deliver the desired ADME-tox profile of the target candidate profile (TCP).

Multiparameter Optimization

MolGen™ is designed to generate leads, rather than hits, from Day 1, which is made possible by a proprietary set of ADME-tox models (DeepPropR™). The figure shows the accuracy of our 10 most used DeepPropR™ models from a prospective evaluation.

Output of PocketBlueprinter™

MolGen™ – building & iterating compounds

Novel, potent, & selective compound

Hypothesis Generation

Unlike other AI drug discovery companies, DeepCure does not use AI to simply match a library of compounds to a known pocket. Instead, we create causal, data-driven, human-interpretable hypotheses for binding to a given protein target. This enables us to go beyond known binding sites and ligands.

Structure Preparation

We prepare 3D structural models using a combination of publicly available crystal structures, non-public structures, and predicted structures. As part of our proprietary structure preparation protocols, our scientists review a set of data quality metrics for the structures, select reference structures, delete bad structures, and group structures. This ensures the structure(s) used for hypothesis generation is the best representation possible.


For most therapeutic targets, there is no data, limited data, or biased data. PocketBlueprinter™ allows us to generate novel hypotheses by leveraging AI/ML and computational chemistry methods to map the protein surface and identify novel binding modes. The outputs serve as an initial binding hypothesis for our molecular generation tool, i.e. MolGen™.

Causal Analysis

ML methods for drug discovery typically focus on correlations. However, these methods lead to biases for the types of compounds that have failed in discovery and are inadequate for finding truly novel compounds. To overcome these shortcomings, DeepCure uses causal ML to find binding interactions without the biases for failed binding modes and/or previous compound structures.

Medicinal chemists engage in a conversation with explainable models

DeepCure’s platform is designed to be human-interpretable. Causal features can be shown as heatmaps in 3D (or mapped to 2D) for review by medicinal and computational chemists, enabling identification of irregularities or gaps in the models. By seeing how molecules are predicted to interact with the protein, scientists can make rational design changes to the molecule and explore interesting molecular interactions. Human interpretability allows for a feedback cycle that ensures scientists don’t blindly follow the ML algorithm or chemists’ intuition.