🧬 Exploring how genetics-driven drug discovery is shaping RNA-based antivirals against SARS-CoV-2 from antitargets to delivery challenges. Join the discussion at the OTDC-2026 📍Rome, Italy |🗓️May 25–26, 2026 🔗 #RNAtherapeutics# #Genomics# #DrugDiscovery#

The textbook explanation for NSAID gut toxicity? Wrong. Two new studies show the gut microbiome, not prostaglandin suppression, drives GI injury. The microbiome can no longer be an afterthought in drug development. #microbiome# #pharma# #drugdiscovery#

🌐 The Most Far-Reaching Application of AI: Healthcare 🧬💊 In her recent interview, Mu Tou Jie highlighted that AI’s deepest impact will be in healthcare — rewriting the logic of disease treatment, accelerating drug development, and unlocking the potential of multi-omics sequencing. Here are some key players: 1️⃣ Gene Editing & Therapy •CRISPR Therapeutics ($CRSP): One-dose cure for sickle cell & β-thalassemia — freeing patients from 10–20 annual ER visits. •Beam Therapeutics ($BEAM): Base editing tech replaces a single DNA base without cutting strands — lower side effects, ideal for chronic genetic diseases. •Intellia Therapeutics ($NTLA): In vivo CRISPR delivery via LNP to repair genes directly inside the body. 2️⃣ Synthetic Biology & DNA Design •Twist Bioscience ($TWST): High-throughput DNA synthesis platform — supplies “molecular building blocks” to pharma, research, and diagnostics. 3️⃣ Precision Medicine •Tempus AI ($TEM): Combines AI + one of the world’s largest clinical/molecular datasets for personalized treatment & faster drug trials. •10x Genomics ($TXG): Pioneering single-cell & spatial multi-omics tools. 4️⃣ AI-Driven Drug Development •Recursion Pharmaceuticals ($RXRX): Massive automated bio-chemical experiments + AI to discover new disease biology and drug candidates. 5️⃣ Gene Sequencing & Analysis •Illumina ($ILMN): Gene sequencing leader; PromoterAI detects non-coding gene variations, boosting rare disease diagnosis efficiency. 💡 AI in healthcare isn’t just an upgrade — it’s a paradigm shift. From curing rare genetic diseases to designing new drugs in record time, the convergence of AI and biotech is redefining medicine’s future. 🚀 #AI# #Healthcare# #Biotech# #Genomics# #DrugDiscovery#

Drug discovery is becoming a computational science. Richard Bonneau leads AI for drug discovery at @Genentech. Catch him bringing what's actually working to #FullyConnected26#. Sept 29–Oct 1, SF. Register here:

AI drug discovery may be finding its real scaling law. 🧬 As Claude and other frontier models move deeper into biology, the bottleneck is shifting from model capability to proprietary biological data and real-world feedback. That puts companies with unique data assets in focus. $TEM, for example, has built 500+ PB of molecular and clinical data across 45M+ de-identified patient journeys. The next AI drug race may be less about who has the biggest model — and more about who can build the fastest data → prediction → validation → feedback loop. Trade $TEM on StableStock: → #AIDrugDiscovery# #Biotech# #TEM# Not investment advice. For informational purposes only.

AI in drug discovery isn't just a speed play. It unlocks new therapeutic strategies once out of reach. At the CDD & AI for Biologics Summit today, Chai’s Ryan Peckner will share how Chai’s latest models can design precise, novel antibodies for hard targets:

Much of AI in Pharma focuses on drug discovery. Yet bringing innovation to patients takes years of clinical trials and regulatory work. Today we're launching KIRHA Life Sciences to shorten the time between a scientific discovery and the day it helps a patient. We can accelerate the work surrounding clinical trials: designing protocols, preparing CDISC datasets for regulatory submissions, and monitoring drug safety through pharmacovigilance (PV) and real-world evidence (RWE). We've spent 18 months building context infrastructure for AI agents, serving 500,000+ requests through our network of 800+ provider's tools. That taught us what agents need in regulated workflows: verifiable search, curated data, and privacy from inference to context. KIRHA has always been a context company. Now we're also taking on the coding behind some of the most regulated work in the industry, with OpenSAS, our open-source runtime compatible with SAS 9.4. It's already attracting contributions from the scientific community, including Yilong Zhang and members of R Consortium Pilot 7, who have been benchmarking it on agentic SDTM and ADaM pipelines. On Nov 24, we'll show how we're speeding up @CDISC programming with eXYSTAT at DMB Congress in Paris. DM me if you'd like to join.

CZ Flags the Biggest Hurdle for AI Drug Discovery Anthropic (@AnthropicAI) is expanding into drug discovery, including research targeting rare childhood forms of muscular dystrophy. Binance (@binance) co-founder Changpeng Zhao, (@cz_binance) called the development encouraging, but highlighted the potential decade-long process for clinical trials and regulatory approval. He also floated the idea of using AI to review applications, including the possibility of competing AI providers.