DeepCure was founded to accelerate breakthrough science, developed by world-leading AI engineers, data scientists and biologists.
Our founding team includes some of the industry’s preeminent drug-discovery scientists and technologists.
Our vision is to use AI-driven discovery to create better molecules and faster cures for every disease-relevant protein target.
Kfir Schreiber
CEO & Co-Founder
Joseph Jacobson, Ph.D.
CSO & Co-Founder
Thrasyvoulos (Thras) Karydis
CTO & Co-Founder
Georg Duenstl, Ph.D.
VP of Drug Discovery
Han Lim, Ph.D.
Chief Business Officer
Derrick Miyao
VP of Molecular Foundry
Dana Logviniuk, Ph.D.
Senior Scientist, Synthetic Organic Chemistry
Elvira Haimov, Ph.D.
Principal Scientist, Medicinal Chemistry
Julia Antzis
Lab Technician
Daniel Graziano
Senior Machine Learning Scientist
Sara Omlid, Ph.D.
Business Development & Commercial Strategy Lead
Panos Terzopoulos
Senior Machine Learning Scientist
William Kaplan, Ph.D.
Principal Scientist, Medicinal Chemistry
Ryan Ratcliff
Associate Software Architect
Pegah Kolahi
Scientist, Computational Chemistry
George Bikos
Senior Software Engineer
Jon Kaufman, Ph.D.
Principal Scientist, Machine Learning
Shaked Bar Cohen, Ph.D.
Scientist, Biology
Michal Segal Salto, Ph.D.
Senior Director, Biology
Derek Miller
Director of Machine Learning
Mark Schroering
Associate Director, Lead Architect
Heather L. Osswald, Ph.D.
Associate Director, Automated Chemistry
Michal Barshaf Yona
Office Manager & Executive Assistant
Matthew Tieman
Senior Machine Learning Engineer
Ohad Hasin, Ph.D.
Lab Operations Manager
Ofir Bar
Principal Scientist, Analytical Chemistry
Steven Ferrara, Ph.D.
Director of Medicinal Chemistry
Chris MacNaughton
Software Architect
Kelvin Chan, Ph.D.
Director of DMPK
Ric Ogden
Director of Finance and Accounting
Nadav Segev, Ph.D.
Senior Scientist, Assay Development and Screening
Orly Dorot, Ph.D.
Senior Scientist, Assay Development
Jacob Gillis
Associate Program Director
Yarden Ziv
Senior Automation Engineer
Jason Deckman, Ph.D.
Senior Scientific Software Engineer
Luke Nam
Automation Engineer
Aayush Gupta, Ph.D.
Senior Scientist, Computational Chemistry
Sivan Louzoun Zada, Ph.D.
Senior Scientist, Organic Chemistry
Ayelet Wagner Azran
Director of People Operations
Kfir Schreiber
CEO & Co-Founder
Joseph Jacobson, Ph.D.
CSO & Co-Founder
Gerald Chan
Director
Yonatan Mandelbaum
Observer
Shahar Tzafrir
Director
Thrasyvoulos (Thras) Karydis
Observer
John Baldoni, Ph.D.
Ex SVP, DPU Head, In-Silico Discovery, GSK Pharmaceuticals
Marti Head, Ph.D.
Director Joint Institute, Oak Ridge National Laboratory
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.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
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).
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
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.
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™.
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.
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.