Dr. Ricardo Godinez-Moreno

An AI-ready lab notebook for life science doesn't need to be complex or expensive. At Pioneer Labs, we use a lab management system built entirely with generic software tools. It's simple, cheap, and easy to modify. Thanks so much to ElevenLabs for their original Notion ELN template!! 🫶🙏 We have gotten such good use out of it, and by writing this guide I hope to pay it forward! Check out the blog post: https://lnkd.in/euzGi8h8 for: - How we use it - How to use it yourself - Information on the corpus we've amassed - if you're an AI researcher and think you could get insights out of this, reach out!

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Αpproximately 20% of patents are expected to expire by 2035, and then what? Oh, you know already, where that will lead to: The entrance and flooding of generics into the market, while the pharma market will face a $200b (and up to $300b) in revenue losses. The golden age of blockbusters is about to FINISH. Harsh, but it's true, and the industry has to get up to speed, there's no way escaping this! This is where bold leadership is needed, and not just over-relying on outsourcing deals for filling up pipeline needs. Today, finding the next blockbuster is like finding a needle in a huge haystack, and it's become a very tough challenge for many big pharmas. And unequivocally, it's an even bigger challenge for smaller-medium biotech corps that lack the sufficient capital to fulfil the needs to enter the market at the best possible terms. $2t (yes, trillions!) from around 30 drugs between 1997 and 2024, will POOF. Then, senior leaders have to expect a future where only 10 drugs may achieve blockbuster status, and please focus on the modal verb "MAY". The combined revenue potential of these 10 drugs? Somewhere-north of $350b. Understanding the behavioural aspects of decision-making within this market context, and the challenges faced by senior pharma leadership teams, should alert in-house strategists and execs respectively. Cognitive biases, risk perception, sunk cost fallacies, social influences, and information asymmetry all play their (negative) role, with leading decision-makers in the pharma industry needing not only to "read" where the market goes, but also to: - Avoid overconfidence in the success of newly licensed-in drugs - Set aside anchoring on the previous cashflows due to those dreamy blockbuster drugs - Conduct meticulous DD for investments and M&A deals for pipeline diversification - Pivoting into novel, modular, dynamic strategies that can de-risk market changes - Place short-term and long-term goals for acquiring the intel and data for pipeline portfolio management, e.g patient needs, or the efficacy of new treatments out there, and reach closer to symmetry than asymmetry If you don't consider the above (and trust me there are more to have in mind) this can lead to poor decision-making and missed opportunities. So, pharma leaders should ask themselves: Are you ready to embrace this change? Are you prepared to challenge the status quo, promote truly science-backed innovation (and not clinical trial metrics muddle-up-ization) and show acts of adaptability in your strategies? The decisions made today will shape the future of the pharmaceutical industry for years to come. It’s time for you (the leaders) to step up, to think critically, and to act decisively. A reality check is imminent, with tensions rising from the geopolitical trade- and innovation-war front (especially between the US & China), and I hope it will send away some ripples of urgency through the ranks of senior leadership in our industry.

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I just broke down 3 GTM stacks by price and use-case: 🔹 GTM Starter ($1K/month) → Apollo.io - Solid data source with built-in outreach → Clay - Lightweight enrichment and workflow engine → Smartlead or Instantly.ai - Deliverability-focused cold email at startup-friendly pricing → folk - Simple, visual CRM for tracking deals without friction → Expandi.io - Budget LinkedIn automation for solopreneurs and lean teams ✅ Perfect for early teams looking to launch fast with a tight budget 🔸 GTM Growth ($5K/month) → Telescope - Higher-accuracy contact data and firmographics → Clay - Automates deeper, multi-source enrichment → Smartlead or Instantly.ai - For larger sending volume and multichannel support → Breakcold - Social selling to pre-warm leads via engagement → Expandi.io or lemlist - Scale LinkedIn with personalization and multi-seat support → Make - No-code builder to connect GTM systems end-to-end → Trigify.io - Real-time signal-based outreach triggers ✅ Great for teams scaling outbound and layering smarter workflows 🔶 GTM Advanced ($8K+/month) → FullEnrich - Enrichment at scale with company hierarchies and buying signals → Clay - Custom logic, API chains, and enterprise-grade automations → Smartlead or Instantly.ai - Deliverability controls + analytics for enterprise campaigns → HubSpot - Unified CRM + outbound coordination with full funnel visibility → Expandi.io or lemlist - Team-level LinkedIn orchestration with warm-up tools → n8n - Power-user automation with conditional logic and self-hosting → Common Room - Community and social intent scoring in your pipeline → Relevance AI - Hyper-personalization powered by vector embeddings and AI agents ✅ For GTM teams optimizing every step with data, automation, and precision Each stack fits a different growth stage, but the goal stays the same: Predictable, scalable outbound - without the chaos. 💬 Which tools are non-negotiable in your stack? Curious to see what makes your cut.

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This biotech startup is arguably pioneering "generative genomics," using AI to simulate gene expression with lab-level accuracy. 🧬 Andrew Dunn at Endpoints News just shared an exclusive look at Synthesize Bio, a stealthy biotech startup founded by Jeff Leek and Robert Bradley. After two years of quiet development, they've unveiled GEM-1, an AI model trained to predict the outcomes of gene-expression experiments, with claimed accuracy comparable to real-world lab results. A new preprint shows GEM-1 performs about as well as the best possible lab benchmarks (link in the comments if you like to read). The company, backed by a $10M seed round raised in 2023, is based in Seattle and has just 15 employees. 🧬 So what exactly is “generative genomics”? And what is GEM‑1 actually doing? In traditional genomics workflows, scientists run wet-lab experiments to measure gene expression under different conditions. It’s expensive, slow, and biologically noisy, and you only get results after doing the actual biology. GEM‑1 flips that around, according to the company claim. It’s a generative AI model trained on the largest curated RNA-seq dataset ever compiled, tens of thousands of publicly available gene expression profiles from diverse conditions, cell types, and perturbations. The input is a description of the experimental conditions, e.g. which genes are knocked out, cell types involved, treatments applied. The output is a synthetic gene expression profile, a digital prediction of what the RNA-seq data would look like under those conditions. 🧪 Instead of running a costly and time-consuming lab experiment, researchers will hopefully be able to now simulate it in seconds with high biological fidelity. This is why Madrona Venture Labs (and others) are calling it the beginning of the “generative genomics” era, akin to what foundation models have done for text, code, and images. 🔁 And because the model is built as a foundation layer, it can be fine-tuned or adapted to specific disease contexts, cell lines, or therapeutic areas. Bold claims... but this is the type of AI in bio I was exactly meant when we were writing "Beyond Legacy Tools: Defining Modern AI Drug Discovery for 2025 and Beyond" paper... (linking in the comments too). It is a way to go holistic about biology, and I think it is where the real AI-enabled transformation is. Not in incremental improvement of old processes (like virtual screening), but in creating completely new ones... Image credit: Endpoints News

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ALL-IN-ONE AI TOOLS FOR RESEARCHERS IN 2025 Transform your research workflow with these powerful AI-powered tools: SciSpace - Find papers & topics, extract data from research papers, read and chat with papers. Paperpal - Language suggestions, access 250M+ research articles, paraphrasing & plagiarism checks Otio - Summarize & compare documents, chat with all of your documents, generate automatic AI workflows, draft reports & edit text ResearchPal - Write essays, find & extract data from papers, generate citations & create reference library Paperguide - Chat with papers & URLs, get science-backed answers to your questions, find & extract data from research papers Elicit - Find research papers, summarize papers & extract data, find themes & concepts across papers NotebookLM - Chat with & summarize documents, webpages & videos, take notes, create study guides, and podcasts AnswerThis - Find, Read & summarize papers, Find research gaps, write and rewrite text, conduct bibliometric analysis, and create diagrams. _____________________________________________________ Subscribe to my FREE Newsletter for tips on AI in academic research! 👇 https://lnkd.in/dTZesqZA -------------------------------------------------------- I'm Muhammad Irfan 🧬 Follow me and hit the 🔔 for more useful insights ! Repost, if you find this useful 🔁 --------------------------------------------------------

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What if the most important breakthrough in drug discovery isn’t a new molecule—but a living, learning digital twin of the human body? The challenge perhaps is not to design the next therapeutic but to shorten the path and odds from design, discovery and approvals by a near 100% bio-simulation of the human system. At the intersection of AI and quantum computing, we’re entering an era where biosimulation could: • Predict drug safety and efficacy before the first clinical trial • Model complex biological systems in ways no supercomputer alone could achieve • Personalize treatments by simulating how an individual’s biology will respond—long before they take the first dose We’re already seeing momentum: • NVIDIA BioNeMo is enabling large-scale biological language models to simulate protein interactions at atomic precision. • SandboxAQ is using quantum-inspired algorithms to accelerate drug discovery and molecular design. • Quantinuum is partnering with pharma to model complex molecular systems that classical computing struggles to handle. AI brings pattern recognition at unprecedented scale. Quantum offers the computational depth to simulate interactions and dynamics once thought impossible. Together, they could compress years of R&D into months, cut trial failures, and make precision medicine truly scalable. If we can model life this accurately—shouldn’t biosimulation be at the core of how we design and approve the next generation of therapies?

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Want a quick overview of biotech funding in Europe this week? 12 companies raised fresh capital, across all stages. ‣ PRE-SEED: DareWin Evolution 🇫🇷 Improves microalgae productivity for high-value molecule production. → Amount not disclosed ‣ SEED: Bioherent 🇪🇸 Builds photonic biosensors to simplify clinical diagnostics. → €2M Prozymi Biolabs 🇬🇧 Uses enzymes to create gluten-free wheat-based products. → Amount not disclosed ‣ SERIES A: One Biosciences 🇫🇷 Applies single-cell transcriptomics in precision oncology. → €15M ‣ PRIVATE EQUITY: Medyria 🇨🇭 Develops catheter navigation and sensing technology. → CHF1.5M ‣ DEBT FINANCING: Laboratorios Rubió 🇪🇸 Develops and markets treatments for rare diseases. → €4M ‣ CONVERTIBLE NOTE: Umecrine Cognition AB 🇸🇪 Targets brain disorders that affect cognition and alertness. → SEK24.6M ‣ GRANT: Oncomatryx Biopharma 🇪🇸 Creates drugs and diagnostics for the tumour stroma. → €2.5M ‣ POST-IPO EQUITY: AlzeCure Pharma AB 🇸🇪 Focuses on Alzheimer’s and pain therapies. → SEK7M SynAct Pharma 🇸🇪 Targets inflammation via melanocortin receptors. → SEK17.7M GenSight Biologics 🇫🇷 Develops therapies for retinal and CNS diseases. → €500k ‣ POST-IPO DEBT: OKYO Pharma 🇬🇧 Works on non-opioid pain and eye disease drugs. → $1.9M Want updates like this every week? Follow me. Turn on notifications. — I'm Sébastien, founder of Mywebtechcare®. I help life science founders grow their visibility and attract long-term opportunities on LinkedIn.

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