DeepCure Closes $40M Series A Funding Led by Morningside Ventures

Media Inquiries:  Kimberly Ha, KKH Advisors kimberly@kkhadvisors.com +1 917-291-5744

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Series A financing will catalyze growth and advance novel small molecule therapeutics pipeline across various disease indications

The funding will also be used to build an automated robotic wet lab to complete DeepCure’s vision of end-to-end AI drug discovery

Boston, MA – November 8, 2021 –DeepCure, a leader in developing novel, small molecule therapeutics using the world’s largest AI drug discovery engine, announced today the closing of a $40 million Series A financing round led by Morningside Ventures, with participation from existing investors TLV Partners, Sapir Venture Partners, and Benon Group Ltd. The Company has raised a total of $47 million in financing since its inception.

Deepcure is charting a new frontier in drug development using its AI-driven drug discovery platform and automated, robotic laboratory. The Company is developing several novel preclinical candidates across different “undruggable” classes, ranging from polypharmacology programs to protein-protein interactions and previously “impossible” specificity challenges critical in various indications in areas of high unmet medical need. With shorter timelines to develop the highest-quality drug candidate, DeepCure’s technology ensures the discovery and development of novel, small molecule drugs that were previously undiscoverable using conventional methods.

“We are delighted to welcome a top-tier syndicate of leading healthcare investors led by Morningside Ventures that support our vision of developing an end-to-end drug discovery pipeline designed, optimized, synthesized, and analyzed by AI,” said Kfir Schreiber, CEO, and Co-Founder, DeepCure. “We have made tremendous progress since founding DeepCure a few years ago, and this financing reflects strong support for our technology platform, people, strategy, and most importantly, our mission to accelerate the discovery of novel targets and therapies which were previously undruggable.”

The proceeds from the Series A financing will be used to expand DeepCure’s pipeline with five additional oncology programs and develop an automated robotic wet lab to fulfill its vision of developing drugs that are fully designed, synthesized, and tested by AI. The funding will also be used to double the headcount of its current drug discovery scientists and technologists next year, including a global expansion with the launch of two new sites in Greece and Israel.

“This investment positions us to advance our first novel small molecule compound to file for IND, advance our pipeline and realize our vision of making a significant impact for patients,” added Joseph Jacobson, Ph.D., Chief Scientific Officer, and Co-Founder, DeepCure. “We scan our entire proprietary molecular database for optimal drug candidates for each discovery program, with all of our state-of-the-art in-house AI property models simultaneously. Thus, our approach increases the effective search space beyond what was currently possible, either computationally or experimentally.” remarked Thrasyvoulos Karydis, Chief Technology Officer & Co-Founder, DeepCure.

“DeepCure’s advanced drug discovery platform is further evidence of the power of data in the realm of life sciences,” said Stephen Bruso of Morningside Ventures. “By pairing the world’s largest molecular database with sophisticated AI algorithms, DeepCure has the ability to deliver novel small molecule therapeutics to patients with significant unmet need. We are proud to partner with them in this endeavor.”

About DeepCure

Deepcure was founded to accelerate breakthrough science, developed by world-leading AI engineers, data scientists, and biologists. Using an end-to-end drug discovery platform and automated robotic wet lab, the Company is developing a novel pipeline of precision small molecules across many different therapeutic indications. DeepCure’s founding team includes some of the industry’s preeminent researchers and technologists. Our vision is to use AI-driven discovery to create better small molecules therapeutics and faster cures for every disease-relevant protein target. DeepCure is based in Boston, MA. For more information, visit http://www.deepcure.ai/.

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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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MolGen™

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.

PocketBlueprinter

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.