How AI is Changing Drug Development

Stacie Calad-Thomson started her talk on the impact of #AI in #drugdevelopment, highlighting that while AI provides frameworks and insights, it's not a silver bullet. AI is a game-changer, significantly expediting the drug discovery process. A journey from traditional drug discovery to AI-driven applications: The Problems: Traditional drug discovery challenges, including a high failure rate, data silos, complexity of biology, and inefficient design-make-test cycles. The process takes a decade and costs $billions. 1- Target Identification: Companies like Recursion use lab #automation and multi-omics (#invivomics, #phenomics, #metabolomics, #proteomics, #transcriptomics, #genomics) data to map biological relationships and run 2 Million experiments weekly. They leverage NVIDIA GPUs and recently secured a $50M investment, and acquired Cyclica & Valence Labs. By integrating AI, bridge the gap between #Biotech and #Techbio. 2- HIT Screening: Open source tools like AlphaFolio have revolutionized drug discovery by predicting 3D protein structures, enabling rapid in silico screening and precise target design. 3- Lead ID: BenevolentAI generative molecular design and active learning facilitate the rapid identification of potent drug candidates, such as #Percipinib, Eli Lilly and Company, for COVID treatments. 4- Lead Optimization: Exscientia combines generative molecular design with active learning for multi-parameter optimization, streamlining drug development. 5- Preclinical: Exscientia's AI-driven platform improves cancer treatment and outcomes. They achieved remarkable results, including a two-year remission for a chemotherapy-intolerant patient at a fraction of CAR-T costs. 6- Clinical Trials: Predicting disease severity and patient stratification for #COVID-19 clinical trials. As CSO at BioSymetrics, Stacie outlined their platform's capabilities, promising phenomics-driven hit discovery in less than a year, with a timeline covering gene-disease drivers, in-vivo modeling, hit identification, and target identification. Stacie contributes to responsible and ethical AI in healthcare as a board The Alliance for Artificial Intelligence in Healthcare (AAIH), collaborating with regulators. AI is undoubtedly transforming drug development, and Stacie's insights shed light on its immense potential. Following her talk, the panel moderated by Anjali Pandey, SVP Sudo Biosciences, Frazier Life Sciences, engaged panelists on a similar topic. Nitin Kumar, CEO, Nuron.IO, Sachin Sontakke, Sr. Dir Gilead Sciences, Preetha Ram, CTO Pier 70 Ventures. The consensus was that AI has and will change healthcare and how patients are cared for.  Since 2019, AI drug discovery start-ups had 352 deals and raised $10B from 600 unique investors. 80% of this $10B was invested in the top 30 companies. The tech-first is the most appealing to investors. The investment gap remains in manufacturing. Big thanks Anurag Mairal, PhD (He/His), Ashutosh Shastry & Pushkar Hingwe

This new AI tool can design antibody drugs 100x faster than traditional methods - without trial and error. It’s called Chai-2. And it might quietly change how we discover drugs. Here’s why: Normally, designing an antibody drug takes months of testing in the lab - just to find one molecule that works. But Chai-2, an AI model built by Chai Discovery, skips that entire process. It watched hours of real-world data and learned to generate antibody candidates from scratch - with atomic-level precision. This new AI tool can design antibody drugs 100x faster - and with zero trial-and-error. In early tests on 52 new antigens, it produced viable hits for 50% of them - in just two weeks. That too without any screening or manual lab trials. The team calls it “Photoshop for molecules.” And the analogy makes sense - it lets researchers design antibodies with programmable control, rather than waiting for randomness to deliver results. This matters for two big reasons: ▶ 1. It’s faster and cheaper Fewer experiments means lower R&D costs and faster GTM. Especially powerful for early-stage biotechs running on tight timelines and capital. ▶ 2. It’s not just about antibodies Chai-2 can design miniproteins, explore new formats, and expand the kinds of molecules we can even consider therapeutically. As a funding coach and investor in healthtech, I see a signal here: → Founders who can compress the drug discovery loop - even slightly - will unlock investor confidence faster. Would you bet on a drug designed by AI if it meant saving months (and millions)? #entrepreneurship #healthtech #innovation

A few years ago, patient recruitment was one of the biggest bottlenecks in clinical trials. Finding the right participants took months—sometimes years—delaying critical treatments. Then AI entered the picture. Suddenly, sponsors and sites could identify eligible patients in record time. Recruitment delays? Reduced. Data management used to mean hours of manual entry and cleaning, with human errors slipping through. Now, AI automates the process, detecting inconsistencies in real time. Monitors used to sift through mountains of data to spot risks and protocol deviations. Today, AI flags potential issues before they escalate, strengthening risk-based monitoring. And let’s not forget drug development. What once took decades is now moving at a pace we never imagined— AI is predicting molecular success rates, refining trial designs, and helping bring treatments to market faster. But here’s the thing: AI isn’t replacing us. It’s making us better, faster, and more efficient. The shift is happening. Are you ready? Let’s talk.

#AI is shattering the drug-development clock cutting work that once took a decade (and ≈ $2.6 billion per approval) down to a fraction of the time What the new pace looks like: • Discovery in months, not years. #Insilico Medicine, #Recursion and #Exscientia now reach a pre-clinical candidate in just 9-18 months instead of the traditional 40-50 months log. • Fewer molecules, smarter picks. Insilico tests 60-200 compounds per project; old-school programs often screen 3,000-5,000 • First AI-designed drug in the clinic. DSP-1181 went from concept to Phase 1 in 12 months (vs 4-5 years) • #AlphaFold speed run. Researchers used the model to pinpoint an ideal lead in 8 hours—a task that normally lasts a month • Clinical trials on the near horizon. Google #DeepMind’s Demis Hassabis expects multiple AI-designed drugs to enter human studies before the end of 2025, forecasting timelines that drop from years to “months or maybe even weeks” Why this matters: quicker pivots on promising science, lower attrition, and potentially fairer pricing when R-and-D costs fall. Your take: Will AI-first pipelines deliver blockbuster therapies faster or will regulatory and data-quality challenges slow the momentum? Let’s discuss. #AIinHealthcare #DrugDiscovery #PharmaInnovation #MachineLearning #FutureOfMedicine #DrGPT

💊 Imagine prescribing the right cardiovascular drug, at the right dose, for the right patient—every time. Thanks to artificial intelligence, that future is closer than we think. A new European Heart Journal review explores how AI is transforming cardiovascular pharmacotherapy—from predicting who benefits most from antihypertensives to personalizing statin dosing, enhancing adherence, and even accelerating drug discovery through in silico models. What stood out to me: 🫀 ML models targeting therapy personalization across hypertension, diabetes, CAD, heart failure & thrombosis 🫀 AI tools identifying drug–drug interactions and side effect risks before they happen 🫀 Generative models shaping smarter, leaner clinical trial designs 🌎 But the gap is clear: few models are externally validated or ready for real-world use. To unlock true impact, we need rigorous validation, standardized frameworks, and a strong focus on equity and generalizability. What will it take to move AI from potential to practice in cardiovascular care? #AIinHealthcare #Cardiology #Pharmacotherapy #DigitalHealth #MachineLearning #PrecisionMedicine #DrugDiscovery #HealthInnovation #ClinicalTrials #HealthTech

At Biogen, cutting-edge technologies like AI and Generative AI are transforming how the firm develops therapies for complex neurological diseases like Alzheimer’s, Parkinson’s, and ALS. 🌟 From accelerating drug discovery to optimizing clinical trials and advancing personalized medicine, AI is making a profound impact. 💡 Here’s what they are doing: ✅ Drug Discovery: Using AI to analyze massive datasets and design new molecules faster than ever. ✅ Clinical Trials: AI-powered patient recruitment and real-time monitoring through wearables are improving trial efficiency. ✅ Personalized Medicine: Tailoring therapies to individual patients with machine learning insights. ✅ Supply Chain Excellence: Predictive analytics to forecast demand, optimize inventory, and enhance efficiency. 🌐 Why it matters: AI has the power to bring therapies to market faster, improve patient outcomes, and drive operational efficiency across the biopharma industry. It’s not just about innovation—it’s about transforming lives. 💙 📈 The future of healthcare is being shaped by AI, and Biogen is leading the way. But we’re also mindful of the challenges—data privacy, model transparency, and evolving regulations are critical areas we’re navigating to ensure ethical and impactful AI use. 🛡️ 💬 What are your thoughts on AI’s role in biopharma? Let’s discuss how these technologies can unlock new possibilities in healthcare! #AI #GenerativeAI #Biopharma #Innovation #HealthcareTechnology #Neurology #FutureOfMedicine #Biogen 🌟🧬

What if we could teach AI to think like a cell? Predicting a drug’s effect on the body is one of the hardest problems in medicine. Traditional AI models focus only on molecular structure but, that’s not enough. The same molecule can trigger different gene expressions and morphological changes depending on the cellular environment. A new AI model, InfoAlign, 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝘀 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗲𝗻𝗲 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗰𝗲𝗹𝗹 𝗺𝗼𝗿𝗽𝗵𝗼𝗹𝗼𝗴𝘆 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗯𝗶𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹𝗹𝘆-𝗮𝘄𝗮𝗿𝗲 𝗱𝗿𝘂𝗴 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀, dramatically improving drug discovery. 1. Outperformed 27 baseline models in molecular property prediction, improving accuracy by up to 6.4% across 685 tasks. 2. Enabled 𝘇𝗲𝗿𝗼-𝘀𝗵𝗼𝘁 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗲-𝗺𝗼𝗿𝗽𝗵𝗼𝗹𝗼𝗴𝘆 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴, accurately linking chemicals to cellular responses without retraining. 3. Removed redundant information in drug representations using an information bottleneck, improving generalization across datasets. 4. Captured cross-modal relationships by modeling molecular and genetic perturbations in a context graph. InfoAlign’s explicit minimization of redundant information (e.g., confounding batch effects) improves the generalizability and robustness of molecular representations. This is crucial in drug discovery, where spurious correlations can lead to unsafe predictions regarding toxicity or efficacy. Such alignment with true biological responses reduces risks inherent in overfitting to technical noise. This ensures that predictions remain interpretable and reliable. Here's the awesome work: https://lnkd.in/g9DdJ7eD Congrats to Gang Liu, Srijit Seal, John Arevalo, Zhenwen Liang, Anne Carpenter, Meng Jiang, and Shantanu Singh! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

Generative AI in Drug Discovery 🌟 Just like how ChatGPT crafts texts and DALL-E 2 creates lifelike images from prompts, generative AI is now transforming drug discovery. Imagine AI generating new drugs for diseases like cancer, Alzheimer's, and fibrosis! Some notable examples: → AI-driven platforms like Insilico Medicine's PharmaAI are designing drugs for complex conditions. For instance, a USP1 inhibitor for solid tumors is in clinical trials, and a QPCTL inhibitor for malignant tumors is being developed with Fosun Pharma. Their lead drug for idiopathic pulmonary fibrosis has reached phase II trials! → Recursion is using AI to develop treatments for rare diseases like cerebral cavernous malformation and neurofibromatosis type 2, both in phase II trials. → Healx is repurposing existing drugs for rare diseases, targeting conditions like Fragile X syndrome and cancers like plexiform neurofibroma. Over the years, AI's role in drug discovery has grown exponentially. Traditional methods take 10-15 years and billions of dollars to bring a drug to market. Generative AI accelerates this process, identifying targets, designing drugs, and even predicting clinical trial outcomes with tools like inClinico, boasting a 79% accuracy rate! The synergy between vast datasets, expert knowledge, and AI capabilities is reshaping the future of medicine. Companies like GSK, Novartis, and Roche are building in-house AI capabilities, while collaborations between biotechs and pharma giants are on the rise. From 4 partnerships in 2015 to 27 in 2020, the trend is clear! In the image, we see how Machine Learning algorithms are utilized in the biomedical field, closely related to drug discovery, to innovate new treatments and therapies. As we witness this pivotal moment, the potential for AI-designed drugs to reach the market is within sight. The journey of integrating AI in drug discovery is just beginning, promising a future where medicine is more precise, efficient, and innovative. #productivity #machinelearning #technology #artificialintelligence #drugdiscovery #healthcare #biotech #pharma

Google unveils AI-powered healthcare innovations spanning drug discovery, enhanced search, and integrated medical records: 💊In drug discovery, new open AI models (TxGemma) are designed to understand both text and molecular structures to help predict the safety and efficacy of potential therapies 💊An AI co-scientist tool built on Gemini 2.0 assists biomedical researchers by parsing scientific literature, generating novel hypotheses, and proposing experimental approaches 💊These tools will be available through the Health AI Developer Foundations program, aiming to streamline the early stages of drug development 🔎 In search, expanded health knowledge panels now cover thousands more topics and use AI to provide quick, credible answers to health-related queries 🔎 The "What People Suggest" feature aggregates user discussions from online platforms to offer personalized insights based on shared experiences with specific health conditions 🔎 These enhancements support multiple languages, including Spanish, Portuguese, and Japanese, and are initially rolling out on mobile devices in the U.S. 💿The global launch of Medical Records APIs for the Health Connect platform on Android enables apps to read and write standardized medical data, such as allergies, medications, immunizations, and lab results 💿The APIs support over 50 data types, integrating everyday health tracking with official medical records from healthcare providers 👇Links to source articles in comments #DigitalHealth #AI #Google

AI is already starting to dictate drug development - favoring the tried and true over the innovative unknown. This scenario is becoming a reality in the pharmaceutical industry, as AI begins to play a pivotal role in predicting the success of drug trials. A recent article highlights AI's growing influence, but at what cost to medical breakthroughs? https://buff.ly/3NGIDmI IMPACT The reliance on AI for decision-making in drug development risks ushering in an era of 'safe bets'. Much like the film industry's penchant for sequels and franchises over original scripts, the pharmaceutical world may lean towards developing the '19th beta blocker' or the '12th edition of a Marvel movie'. This approach, while financially safe in the short term, could stifle the emergence of novel therapies that address unmet medical needs. LONG TERM IMPACT Moreover, this trend poses a long-term challenge. As reimbursement models evolve, they may begin to favor truly innovative treatments, leaving behind those that are merely incremental improvements. This shift could catch companies off guard, especially those that have played it safe, guided by AI's predictive analytics. In essence, we risk training our pharmaceutical research to 'study for the test' rather than to 'learn and innovate'. RECOMMENDATION The need for balance is clear: while AI can streamline initial drug trial processes, it should not be the sole arbiter of what gets developed. The pharmaceutical industry must nurture a culture of innovation that allows for both data-driven decisions and creative risks. Only then can we ensure that our medical future is not just a repetition of the past but a landscape rich with groundbreaking therapies. #AI #Pharma #DrugDevelopment #innovation