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CHARM Therapeutics

CHARM Therapeutics utilizes its proprietary DragonFold technology, which employs 3D deep learning for protein-ligand co-folding, to develop small molecule inhibitors targeting previously undruggable proteins associated with cancer and other diseases. By addressing the challenge of the vast majority of the human proteome remaining undruggable, the company aims to create transformative therapies for patients with high unmet medical needs.

London, United KingdomFounded 2021627K+ followers
Updated 20 months ago

Funding

$70M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

N
Funding rounds are not available yet.

Founders

Product

Problem

A significant portion of the human proteome is considered "undruggable," meaning traditional drug discovery methods struggle to identify small molecule inhibitors for these proteins. This limitation hinders the development of effective therapies for diseases, including cancer, where these undruggable proteins play a critical role.

Solution

CHARM Therapeutics is developing small molecule inhibitors against previously undruggable protein targets using its proprietary DragonFold technology. DragonFold employs 3D deep learning to predict protein-ligand co-folding, enabling the design of novel molecules that bind to challenging protein surfaces. By accurately and rapidly modeling protein-ligand interactions, CHARM aims to unlock new therapeutic opportunities for cancers and other diseases with high unmet medical needs. The company combines its AI-driven approach with state-of-the-art lab facilities to advance a pipeline of innovative small molecule inhibitors.

Target Audience

The primary target audience includes patients with cancers and other diseases for which there are currently limited or no effective treatment options due to the undruggability of key protein targets.

Features

  • DragonFold technology: A proprietary 3D deep learning algorithm for protein-ligand co-folding prediction.
  • Identification of novel binding pockets on previously undruggable proteins.
  • Structure-based drug design for small molecule inhibitor development.
  • In-house laboratory facilities for compound synthesis, screening, and validation.
  • Focus on oncology and other diseases with high unmet medical needs.
This profile is AI-generated and may contain inaccuracies.