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.
Co-Founder
Thrasyvoulos (Thras) Karydis
CTO & Co-Founder
Luca Rastelli, Ph.D.
Chief Scientific Officer
Han Lim, Ph.D.
Chief Business Officer
Derrick Miyao
VP of Molecular Foundry
Derek Miller
VP, Head of Platform Research
Georg Duenstl, Ph.D.
VP of Drug Discovery
Kfir Schreiber
Director
Joseph Jacobson, Ph.D.
Director
Thrasyvoulos (Thras) Karydis
Director
Gerald Chan
Director
Jason Dinges
Director
Ehsan Jabbarzadeh, PhD, MBA
Director
Shahar Tzafrir
Director
Alex Kash
Observer
Yonatan Mandelbaum
Observer
Wasim Akhtar
IT Specialist
Panos Terzopoulos
Senior Machine Learning Scientist
Michelle E. Fodor
Associate Director of Biophysics and Structural Biology
Ric Ogden
Director of Finance and Accounting
Allie Higgins
Scientific Program Manager
Daniel Graziano
Senior Data Scientist
Aayush Gupta, Ph.D.
Senior Scientist, Computational Chemistry
Elvira Haimov, Ph.D.
Principal Scientist, Medicinal Chemistry
Pegah Kolahi
Senior Scientist, Computational Chemistry
Matthew Tieman
Senior Machine Learning Engineer
MiJeong Kim, Ph.D.
Director of Immunology
Luke Nam
Senior Automation Engineer
Jon Kaufman, Ph.D.
Associate Director, Physics-Based Machine Learning
Michal Segal Salto, Ph.D.
Senior Director, Biology
Chris MacNaughton
Software Architect
Ryan Ratcliff
Associate Software Architect
Ayelet Wagner Azran
Director of People Operations
Steven Ferrara, Ph.D.
Director of Medicinal Chemistry
Jason Deckman, Ph.D.
Senior Scientific Software Engineer
Carlos Borca, Ph.D.
Principal Scientist, Computational Chemistry
Kelvin Chan, Ph.D.
Director of DMPK
Jacob Gillis
Associate Director, Program Operations
Mark Schroering
Associate Director, Lead Architect
George Bikos
Senior Software Engineer
Heather L. Osswald, Ph.D.
Associate Director, Automated Chemistry
Manual synthesis dramatically stifles innovation in drug discovery for many reasons, including (i) substantial time costs, (ii) high FTE costs, (iii) low synthesis success rates (20-34%), and (iv) a bias towards known reactions.
At DeepCure, we are fixing these problems to unlock the vast chemical space that AI drug design tools want to explore, but don’t, because manually synthesizing such compounds would not be practicable.
Manual synthesis dramatically stifles innovation in drug discovery for many reasons, including (i) substantial time costs, (ii) high FTE costs, (iii) low synthesis success rates (20-34%), and (iv) a bias towards known reactions.
At DeepCure, we are fixing these problems to unlock the chemical space that AI drug design tools want to explore but can’t because it is not practicably available to most chemists.
Synthetic
Steps
Reaction
Types
Reaction
Development
Industry
Standard
1
manual
fully
automated
made possible with automated analytical evaluation, purification, evaporation, etc.
made practicable with automated reaction development
made feasible with miniaturization & quick turnaround (2-10 days)
Our 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).
Output of PocketExpander™
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 use our patent-pending AI methods to 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.
Our hypothesis generation starts with a rigorous analysis of available structural information. Beyond the standard steps involved in structure preparation, our proprietary protocols also include methods for repairing structures (e.g. building missing loops) and generating more robust structures leveraging molecular dynamics (MD).
For most therapeutic targets, there is no data, limited data, or biased data. PocketExpander™ allows us to generate novel hypotheses by leveraging AI/ML and computational chemistry methods to map the protein surface and identify novel binding modes (shown as colored dots). The outputs serve as the blueprints for our molecular generation tool, i.e. MolGen™.
ML methods for drug discovery typically focus on correlations, which lead to biases for the types of compounds that have previously failed in discovery. In contrast, DeepCure uses a causal ML approach to find binding interactions without the biases for fruitless binding modes to design truly novel compounds.
Medicinal chemists engage in a conversation with explainable models
DeepCure’s platform is designed to be human-interpretable. 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 – ensuring we don’t blindly follow the ML algorithm or chemists’ intuition.