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DeepMirror launches early access programme for its intuitive molecular drug design software

publication date: Jan 10, 2024
author/source: DeepMirror

DeepMirror team

From top left: Dr Ryan Greenhalgh (Co-Founder and CTO), Shannon Assaf (Founding Front-End Engineer), Dr Max Jakobs (Co-Founder and CEO), Dr Andrea Dimitracopoulos (Co-Founder and Product Lead), Dr Cecilia Cabrera (Project Manager), Jacob Green (Founding Machine Learning Engineer)


Innovative product enables medicinal chemists to access and integrate AI to boost productivity and creativity, to accelerate therapeutic drug discovery

DeepMirror, a University of Cambridge spin-out company developing intuitive design software for the discovery of novel therapeutic drugs, has announced the launch of its Early Access Programme after a successful closed beta programme during which chemists were invited to test the software over several months. The software allows users to tap into AI-driven insights to improve and accelerate molecular design across the drug discovery pipeline through a secure and user-friendly interface which makes AI-powered drug discovery as simple as using a spreadsheet.

AI-enabled drug discovery programmes often start with pharmaceutical companies partnering with AI companies to deliver insights for their drug discovery efforts. However, this approach requires extensive crosstalk between the two parties, resulting in long waiting times and large amounts of resources spent on both sides. DeepMirror aims to solve this issue by enabling R&D teams to carry out AI-driven research from day one, with seamless workflow integration and without the need to engage external stakeholders, develop internal teams or software, or relinquish any intellectual property. To join the waitlist for the Early Access Programme sign up here.

DeepMirror’s purpose is to fast-track the drug discovery process, for example in the Hit-to-Lead and Lead Optimisation phases, with the ability to predict relevant properties such as drug binding, (bio-)activity, and toxicity, both from user data and from large proprietary curated databases. Laboratory results can be used to refine predictions and generate novel drug candidates for further experimentation, ultimately accelerating the drug discovery process by up to 4 times as estimated by the Wellcome Trust and the Boston Consulting Group (1).

Dr Max Jakobs, Co-Founder and CEO of DeepMirror, commented: “Our mission is to make AI-powered drug design as simple as browsing the web. After 12 months of development and a successful beta testing programme, we are excited to officially launch DeepMirror to early adopters. We are inviting researchers to get in touch to use our secure and user-friendly AI platform for drug design. DeepMirror was already used on active drug discovery programmes and led to the discovery of novel lead series and inspired the synthesis of novel compounds.”

Dr Andrew McTeague, Senior Scientist, Medicinal Chemistry at Morphic Therapeutic, said: “DeepMirror is a huge step forward in the democratization of machine learning models and their application in drug discovery. Its user-friendly interface enables medicinal chemists of all levels to deploy this powerful approach in a fraction of the time. The ability to apply DeepMirror’s platform to any desired endpoint, whether it be potency, selectivity, or even ADME properties, empowers its users to make more informed decisions and to do so faster. We’re always looking for new tools to improve the efficiency of our DMTA cycles and DeepMirror helps ensure that no stone is left unturned.”


About DeepMirror

DeepMirror is on a mission to make scientific software as simple as browsing the internet to guide scientists in the century of biology. DeepMirror brings cutting edge AI directly into the hands of laboratory personnel and empowers companies in their journey from discovery to clinic. Founded in 2019, DeepMirror is now a trusted name among global scientific communities. The company's core offering includes a state-of-the-art app for medicinal chemistry teams, enhancing drug molecule design while significantly reducing preclinical drug program costs and accelerating the progression from initial hits to lead candidates.



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