Thrilled to see this article being featured and to have worked alongside the amazing team at Ignota Labs on it. This work discusses the difficulty in addressing safety issues within drug discovery, whilst highlighting how in silico tools can be used to turn safety issues around. I encourage anyone curious about how AI, cheminformatics and bioinformatics can be used to tackle toxicity within drug discovery to read the article using the link below. #DigitalDiscovery #drugsafety #cheminformatics #AI
This week, Ignota Labs’ article has been featured by #DigitalDiscovery. Masarone, Beckwith, Lane, Hosseini-Gerami et al. explore the state of the art in toxicology prediction for drug discovery, across a variety of modelling techniques and novel data sources such as organ-on-a-chip studies. Discovering drugs that show therapeutic potential is hard and expensive. But finding drugs which do not have severe side effects is even harder. Safety concerns such as toxicity halt 56% of projects, but despite this, safety assessment is often neglected until the late stages of the discovery timeline. Our AI platform SAFEPATH brings safety to the forefront, combining cheminformatics, bioinformatics, and multimodal data analysis to explain why and how safety issues occur, delivering actionable insights to refine or repurpose drug candidates. To understand the novel data sources utilised in SAFEPATH, read the Perspective article here: https://lnkd.in/eQjn8zNf. #drugdiscovery #AI #drugturnaround #toxicity #drugsafety
