SPECTROSCOPY
looking glass D
Through the DeepMirror’s bespoke
software allows users to tap into AI-driven insights
A new company aims to simplify the relationship between AI and drug developers in a bid to reduce complexity and cost, Nicola Brittain reports
rug discovery is a lengthy and costly process, with pre-clinical development alone costing an average of
£35-55m per drug and taking eight years to bring to market (Wellcome Trust, 2023). Drugs take this long to develop because chemists must painstakingly tweak molecules to increase their eff icacy - AI promises to help automate this process. Although the promise of AI in this realm hasn’t yet lived up to its initial promise to map the entire human body and its likely responses to pharmaceuticals, predictions around which tweaks are likely to work better than others can reduce overall costs signifi cantly and there are a number of interesting developments in this fi eld; not least a new company called DeepMirror. Set up as a spin off from the
University of Cambridge, this team of experts in physics, biology, and AI recently launched their Early Access Programme after a successful closed beta programme during which chemists were invited to test
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the software over several months. The company was set up to develop intuitive design software for the discovery of novel therapeutic drugs. Its bespoke 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’, according to the company. Users are provided with access to short cuts as found by other pharmaceutical companies during the drug discovery process.
AI-ENABLED DRUG DISCOVERY PROGRAMMES AI-enabled drug discovery programmes often start with pharmaceutical companies partnering with AI companies to deliver insights for their drug discovery eff orts. However, this approach requires extensive crosstalk between the two parties, resulting in long waiting times and considerable resources spent on both sides.
DeepMirror has developed a
programme that aims to solve this issue by enabling research and development teams to carry out AI-driven research with workfl ow integration and without the need to engage external stakeholders, develop internal teams and software, or relinquish any intellectual property.
MOLECULAR PROPERTIES TAKE CENTRE STAGE In drug development, predicting the properties of drugs (small molecules) before testing them in the laboratory is crucial to reducing the time and resources required to bring safe and eff ective new drugs to patients. Two main types of property predictions are crucial: properties that describe how ‘drug-like’ a molecule is, such as it’s absorption, it’s distribution in the body, how it gets removed from the body and how toxic it is; and second, properties that describe how good a drug is at binding to its target and exerting an eff ect against a disease (aff inity, potency). Deep Mirror tested 184 AI approaches against 44 public datasets and the research highlighted the need for diff erent AI approaches for diff erent datasets. For example, traditional methods perform better
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