Georg Duenstl, Ph.D​.

VP of Drug Discovery

Having served on DeepCure’s Scientific Advisory Board since its inception in 2018, Georg joined the company as Vice President of Drug Discovery in 2022. He has over 16 years of drug discovery experience emphasizing pharmacology, strategy, and innovation. Before joining DeepCure, Georg was an Associate Research Fellow at LEO Pharma. He worked for more than 13 years in various positions at the company’s Danish headquarters and its Boston-based Science & Tech Hub, where he served as Chief Scientist. Georg studied in Germany and the US and holds a Ph.D. in Biochemical Pharmacology and MSc in Biology from the University of Konstanz. He also completed advanced executive education programs at the Wharton School of the University of Pennsylvania (CPD) and the MIT Sloan School of Management (ACE).
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Molecular Foundry

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.

Automated Robotic Custom Synthesis

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

4-10

manual

91
100+2

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)

Notes: 1 4+ by Q2 2024. 2 Planned to reach by end of 2024.

MolGen™

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

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 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.

Structure-Based

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).

PocketExpander™

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™.

Causal Analysis

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.