ET Pharma’s Post

Who is next? Generative AI platforms drive recent drug discovery dealmaking - check out this article. The potential of generative AI models to accelerate the design of drug candidates looks set to boost the funding raised in recent years by AI-focused companies and is driving dealmaking by major biopharma companies seeking to enhance their pipelines. Will this trend continue? https://lnkd.in/gp65xGhS #biopharma #biotech #lifesciences #generativeai #artificialintelligence #genAI

Isomorphic Labs, an independent entity under Alphabet's umbrella, has embarked on research collaborations with pharmaceutical titans Eli Lilly and Novartis. These deals are to leverage Isomorphic's cutting-edge AI technologies, particularly the latest iteration of Google DeepMind's revolutionary AlphaFold protein folding software, in the pursuit of identifying and designing small molecule therapies targeting various disease segments. please follow merklepal for more tech news #ai #aiandml #artificialintelligence #artificialintelligenceforbusiness #aihealthcare #tech #linkedin

Prepare for shortened drug discovery era 😊 If you wonder why drugs, especially original drugs are often expensive it's because the process of drug discovery and development typically costs billions of dollars and years to develop from start to shelved products. In the US, the FDA enumerates five critical stages in drug discovery: 1.      The Discovery and development phases 2.      The preclinical research phase 3.      The clinical research phase 4.      The FDA Review phase 5.      The FDA Post-Market Safety Monitoring phase Traditionally, these five steps typically took approximately 13 years to develop. Other than the length production phase, out of the 5000 drugs in the discovery phase, only about one drug made it to the market. Who wouldn’t increase a huge markup for their drug after this lengthy and costly venture? However, AI technologies in pharmacology have been instrumental, and a groundbreaking, revolutionary approach proposed in the shortening of the development phase as well as saving a few million bucks. Marius, an AI research Engineer at Exscentia which is one of the leading companies paring AI technology and human science to facilitate AI incorporation in the World says “One of the most exciting aspects of using AI in drug discovery is trying to remove the uncertainty in various observations while looking at a drug.” He would add “We are developing methods that allow us to quantify how likely given uncertainty molecules of interest will become drugs.” Like the rush for western frontiers, the AI-Scientific frontier's general objective is specificity. The use of AI technologies and algorithms in the drug discovery and development phase typically entails identifying potential drug compounds and candidates by combing through vast research databases in a process loosely referred to as supervised learning methods. In this case, the AI technologies are trained based on existing patterns from input-labeled data to predict outcomes on a potential drug. The AI algorithms are typically useful in analyzing the vast data from molecular structure databases, clinical trial data, and biological interactions databases to increase and improve the process of developing new drugs. AI algorithms in drug production have been typically important and instrumental in the entire pipeline of drug production from upstream processes such as target identification and validation to downstream processes such as repositioning of the already manufactured drugs. Sources: Exscientia. (2024, August 7). Precision target. https://lnkd.in/dR6eQ-UK Visan, Anita I., and Irina Negut. "Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery." Life, vol. 14, no. 2, 2024, p. 233. https://lnkd.in/dVk-RF6c FDA. "The Drug Development Process." U.S. Food and Drug Administration, 4 Jan. 2018, https://lnkd.in/dKKpd8Rg.

The emergence of generative artificial intelligence (GenAI) has the potential to reform early-stage drug discovery and development:

Exciting Developments in AI-Based Drug Discovery! This week i would like to share our newsletter highlighting innovations at the intersection of artificial intelligence and drug discovery. AI-Based Drug Discovery Tools Ver 1.0 https://lnkd.in/eVdcy_Z5

How digital innovation is helping therapeutics to get to market faster As AI-designed drugs start to enter large-scale clinical trials, DDW’s Diana Spencer investigates how new digital tools are reinventing and reshaping drug discovery for the future. https://lnkd.in/gVQ-WDws #AI #ClinicalTrials #DrugDiscovery

An interesting article in today's Globe and Mail. AI hasn't lived up to the hype when it comes to pharmaceutical discovery. Bottom line: the problems are still too hard for current foundation models. https://lnkd.in/gBpTdiS7 Maybe the AI teams should start collaborating with the quantum teams working on the same problem: https://lnkd.in/gfm32ZHE

What’s the major limiting factor for #AI/#ML today? And what’s the solution? The Financial Times gathered industry icons Roger M. Perlmutter (CEO, Eikon Therapeutics), Bari Kowal (SVP of Development Operations, Regeneron), and Thomas Miller (CEO and co-founder, Iambic Therapeutics), together with Benchling CEO Sajith Wickramasekara to share their predictions for the future of AI — and insights on how #pharma will get there. Check out their top takeaways from #FTPharma: https://lnkd.in/eSTGFY7g

All the buzz of AI/ML impact on R&D requires the infrastructure and solutions to ingest and process the data. Benchling is ready to support advanced analytics against the data model within our solutions. Are you ready for AI/ML? Benchling is!!! How can we help you?