American College of Toxicology (ACT)’s Post

🚀 The Rapid Growth of AI in Predictive Toxicology 🧬 The AI in Predictive Toxicology market is experiencing remarkable growth, expanding from $0.38 billion in 2023 to an anticipated $1.41 billion by 2028, driven by a strong CAGR of 30.3%. This surge is attributed to increasing regulatory pressures, rising concerns over chemical safety, and the growing demand for more ethical and cost-effective toxicity screening methods. The future looks promising with emerging trends such as explainable AI, personalized toxicity assessments, and the regulatory acceptance of AI-based predictions. Innovations like 3D cell culture models, federated learning, and generative models are set to revolutionize the field. Moreover, strategic collaborations and acquisitions are playing a crucial role in advancing AI-driven drug discovery. Partnerships like SyntheticGestalt and Enamine, along with Clarivate's acquisition of Bioinfogate, highlight the industry's commitment to innovation. As this dynamic market evolves, it's exciting to see how AI will continue to enhance the efficiency and ethics of drug discovery. Stay tuned for more developments! https://lnkd.in/gq2H3Jb3 #ArtificialIntelligence #PredictiveToxicology #DrugDiscovery #Innovation

I am excited to share that our paper "Advancing predictive toxicology: overcoming hurdles and shaping the future" has been published in Digital Discovery #RSCDigital . This article was a project I contributed to during my time at Ignota Labs, a company that continues to be a pioneer in this space. All too often we hear about the power of #AI in drug discovery, but predictive models don't come without their challenges, something we discuss in detail in this perspective article. If you are interested in AI drug discovery, then following Ignota Labs as they continue to build transformative technology to save distressed pharmaceuticals is an absolute must!!! My thanks to all my fellow authors for their fantastic work getting this article out there. Link: https://lnkd.in/d4MMZ5q8

🧪 Transforming Toxicology with QSAR Modeling! Discover how Quantitative Structure-Activity Relationship (QSAR) models are reshaping chemical risk assessment by integrating with Adverse Outcome Pathways (AOPs). This comprehensive approach predicts chemical bioactivity towards specific targets linked to toxicity, minimizing the need for traditional animal testing. 🚫🐭 Learn how cutting-edge machine learning techniques are boosting predictive accuracy for liver, kidney, and neurological toxicities, and paving the way for safer chemical development. From data curation to real-world applications, explore the future of computational toxicology! Read the full blog to dive into methodologies, results, and practical applications -> https://lnkd.in/gSCqB9EF At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #QSAR #Toxicology #AOP #MachineLearning #ChemicalSafety #RiskAssessment #ComputationalToxicology #InnovationInScience

Consider joining RTI International on September 11th, 2024 for a webinar on the use of AI and other new approach methodologies to overcome some of the toughest problems in toxicology, drug development, and biomedical research. To learn more about this webinar, “How to Characterize and Validate AI and In Vitro NAMs for Toxicity Testing”, please visit: https://lnkd.in/g9JKa2NG To register, please visit: https://lnkd.in/ghZe-uTW #Webinar #Toxicology #AI #STEM #Research #Biomedicine #Pharmacology

Is SEND sparking a controversy within nonclinical research by accelerating the development of Virtual Control Groups? Read my experiences of the debate at this year's Society of Toxicology meeting #send #cdisc

Artificial Intelligence in Predictive Toxicology Market: Trends, Growth Drivers, and Key Innovations - WhaTech: Artificial Intelligence in Predictive Toxicology Market: Trends, Growth Drivers, and Key Innovations  WhaTech http://dlvr.it/TGG5wt #ai #artificialintelligence

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

Great Article from Ignota Labs ! Read about SOTA in tox and mechanistic modelling and hypothesis generation — the next stage of ML for real world toxicity prediction!

Another thought-provoking article on Virtual Control Groups. Marc Ellison continues to provide excellent thought leadership on the urgent issues facing the SEND community. Thanks Marc! #VirtualControlGroups #SEND #SENDIG #InSilico #NonClinical #PreClinical

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