SPECTROSCOPY
for low dataset sizes and datasets with affinity measurements, whereas modern AI methods (such as deep learning) perform better at higher dataset sizes and some drug-like property datasets. The team also found that
selecting the right featuriser was also dataset dependent. Featurisers are methods that turn molecular structures into a numerical format that computers can understand. Expert features (properties derived by cheminformaticians) worked best for affinity property datasets; yet molecular descriptors (chemical properties of a molecule) and Natural Language Processing (features derived from letter sequences such as molecules SMILES) worked best for drug-like property datasets. All in all, the company did not
find a single model “to rule them all” in small molecule drug property prediction. By benchmarking a wide range of AI methods across various datasets, Deep Mirror has developed a platform that can intelligently adapt to the specific needs of each dataset.
This adaptability is critical in a field as diverse and complex as drug discovery.
FAST TRACK THE DRUG DISCOVERY PROCESS The company’s technology to fast- track the drug discovery process, for example in the hit-to-lead and lead optimisation phases, can 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 four times as estimated by the Wellcome Trust and the Boston Consulting Group.
THE DEEP MIRROR MISSION Dr Max Jakobs, co-founder and CEO of DeepMirror, said: “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 has already been used on active drug discovery programmes, resulting in the discovery of novel lead series and inspiring the synthesis of new compounds.” Dr Andrew McTeague, senior
scientist, Medicinal Chemistry, Morphic Therapeutic, said: “DeepMirror is a huge step forward in the democratisation of machine learning models. Its user-friendly interface enables medicinal chemists of all levels to deploy this powerful approach in a fraction of the time. Being able to apply DeepMirror’s platform to any desired endpoint, empowers 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." n
www.scientistlive.com
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