AI in Small-Molecule Drug Discovery: Hype vs Reality at Biocom Conference

AI in biotech isn’t coming — it’s already here. Proud to contribute to building smarter, faster, and more scalable solutions at Genyx, LLC. Let’s see where this transformation takes us. #AIinBiotech #MachineLearning #Bioinformatics #Innovation #FutureOfScience #Genyx

AI isn’t the future of biotech, it’s the present.   From drug discovery to multi-omics analysis, teams that embrace AI are accelerating timelines, uncovering hidden patterns, and scaling insights that were impossible just a few years ago. Those that don’t? They’re losing ground fast.   At Genyx LLC, we design custom AI-powered pipelines that fit your research, not the other way around. Whether it’s predictive modeling, automated data processing, or intelligent workflow orchestration. We help you move from data to discovery, faster.   Are you using AI in your research or development workflows yet?   #AIinBiotech #MachineLearning #Bioinformatics #Innovation #Genyx #FutureOfScience

The Alchemist’s Algorithm: How AI is conjuring tomorrow’s cures from code 🧪🤖🧬 Drug discovery has always been part science, part sweat, part “please let this work.” Now AI is turning that process into something closer to search + simulation + iteration—with models proposing molecules, predicting properties, and helping labs decide what’s actually worth synthesizing before spending years and millions. My latest Vetta research piece looks at the shift from: trial-and-error biology → to code-assisted cure design. What’s fascinating here isn’t the buzzword (“AI”). It’s the mechanics: - Target discovery (finding what to hit) - Molecule generation (creating candidates) - ADMET prediction (will it be absorbed / toxic / metabolized?) - Faster learning loops between wet labs and models - And the emerging “picks-and-shovels” ecosystem powering it all (data, compute, tooling, platforms) This is one of those themes where the timeline is not instant-gratification… but the compounding is real. The winners won’t be the loudest—more likely the teams that build durable pipelines, clean datasets, and repeatable approval paths. 🔗 Read the full report here: https://lnkd.in/geXUbkyy⁠ #AI #DrugDiscovery #Biotech #HealthTech #Bioinformatics #MachineLearning #Pharma #Innovation #ThematicInvesting #Research #VettaTrustTech

I’ve spent a lot of time thinking about the "Valley of Death" in drug discovery. For decades, we’ve basically been throwing darts in a dark room and hoping for a bullseye. But what if we finally turned the lights on? AI in drug design isn't just about speed; it's about shifting from brute force to intentional, intelligent prediction. The part that actually excites me? We’re moving beyond "screening" to "generating." A recent study in Nature Biotechnology(https://lnkd.in/db7JqQdG) shows how generative models are now designing proteins that don't even exist in nature but solve very specific biological problems. If you've spent any time in a wet lab, you know the frustration of chasing a lead that goes nowhere for months. AI is thinning that herd before we even pick up a pipette. It’s not about replacing the human element—it’s about giving us a better map so we can actually reach the destination. 🧬 Are we overhyping this, or is the lab of 2030 going to look totally different? Sources: * https://lnkd.in/db7JqQdG

Drug discovery is broken infrastructure. Siloed data, decade-long cycles, researchers buried in manual analysis while breakthroughs hide in plain sight across thousands of papers nobody has time to read. AI agents change the equation entirely. Not copilots. Not chatbots. Autonomous research collaborators — scanning literature 24/7, analyzing molecular data, generating hypotheses, and connecting dots across disciplines faster than any team could. The next great drug won't come from a bigger lab. It'll come from smarter systems that never stop looking. We're building toward that future. The lab of tomorrow runs itself.

#ComputeFreedom Near the end of my biology degree, the human genome became publicly available for the first time. We celebrated. And we missed something critical: 𝑪𝒐𝒎𝒑𝒖𝒕𝒆. The processing power that decides whether data becomes knowledge — or stays noise. The best analyses no longer came from the sharpest minds. They came from whoever had access to the largest data centers. Now the same is happening with AI. A technology already so powerful to reshape entire industries — and one whose capabilities are growing exponentially. Pharmaceutical companies are running AI-generated drug targets through high-throughput screening today. Whoever controls the models controls the progress. And almost unnoticed, the gap to the systems behind the APIs keeps growing. History doesn’t repeat itself. 𝗜𝘁 𝘀𝗰𝗮𝗹𝗲𝘀. What we call “open source” today barely scratches the surface. Behind it: a terminal — connected to infrastructure owned by very few. Infrastructure the vast majority will never see, let alone control. That is not freedom. That is dependency. We need AI that belongs to us: Local. Offline. Transparent. Controllable. Private. 𝐼𝑓 𝑦𝑜𝑢 𝑑𝑜𝑛’𝑡 𝑟𝑢𝑛 𝑖𝑡, 𝑦𝑜𝑢 𝑐𝑎𝑛𝑛𝑜𝑡 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑖𝑡. #ComputeFreedom means: the right to control. Because this must never be just a privilege. It has to become a prerequisite. 𝑊𝑎𝑡𝑐ℎ 𝑡ℎ𝑖𝑠 𝑠𝑝𝑎𝑐𝑒. #ComputeFreedom

Ten years. Two billion dollars. One drug. That number hasn't moved in decades. We have models that predict protein structures and screen millions of compounds. But most AI in drug discovery stops at the insight — then a human copies the output into a spreadsheet and manually plans the next experiment. The bottleneck was never intelligence. It was execution. AI agents change this. Systems that don't wait for prompts but actively participate — scanning research, designing experiments, running simulations, interfacing with lab systems, and learning from every result. Iterating while the team sleeps. The shift: AI moving from tool to collaborator. Not replacing scientists, but compressing the cycle between hypothesis and evidence from months to days. We don't need better models. We need systems that act on what the models already know.

Accelerate your biotech journey with Glassbury AI Did you know that clinical enrollment can be up to 50% faster with the right technology? Glassbury AI is here to revolutionize the way biotech companies operate. By harnessing the power of artificial intelligence, we streamline clinical trials and enhance agility in the biotech landscape. Ready to transform your processes? Let's make innovation happen together! 🚀💡 #BiotechInnovation #ClinicalTrials #AIinHealthcare #GlassburyAI

The Silent Strategist: AI’s “Hidden” Nudges Reshape Pipeline Strategy in Life Science Leader, by Remco Jan Geukes Foppen, Paolo D'Ambrosio, and me. For pharma leaders, the question is no longer just "Does the AI work?" but "What behavior is it promoting?" AI algorithms don’t just process data; they create a Choice Architecture that subtly nudges #RD leaders toward specific strategic paths. By prioritizing "safe" predictable wins, #AI can inadvertently stifle radical innovation or bypass patient diversity. To counter this, Adaptive Behavioral Governance moves beyond technical audits. It monitors how AI shapes human decision-making over time, ensuring the "Silent Strategist" aligns with long-term clinical goals rather than just optimizing for short-term algorithmic speed. Great to collaborate again with Ben Comer and the Life Science Connect team. Read the full piece here https://lnkd.in/dfBjAffq Explainambiguity Think Tank #pharma #biotech #LLM #clinicaltrials

Most biotech boards treat AI like a science experiment. They fund the pilot and then look away. Real governance is not about the technology itself. It is about accountability. We have moved past the "can we do it" phase. Now we are in the "who is responsible when it fails" phase. Successful AI integration requires a cultural shift at the top. It is not just a line item in the IT budget. It is a fundamental part of the strategic roadmap. Boards need to move from passive observers to active architects of AI policy. I have written extensively about this framework for moving from experimentation to execution. Is your board measuring AI by the number of pilots or by the depth of its risk accountability? #AIGovernance #BiotechStrategy #PharmaceuticalLeadership #BoardGovernance