Sophia Xu

Genetic Engineering & Biotechnology News (GEN)  has released its updated rankings of the top 10 biopharma clusters for 2024. We've compiled a historical overview of these rankings from 2016 to the present. Boston/Cambridge retains its position as the leading cluster, with the San Francisco Bay Area holding steady in second place. The BioHealth Capital Region remains at third. New York/New Jersey, San Diego, and Greater Philadelphia are ranked fourth, fifth, and sixth, respectively. The top ten is rounded out by Los Angeles/Orange County, North Carolina, Chicagoland, and Seattle. While there are some minor changes in rankings, the overall landscape of top biopharma clusters remains consistent.

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🚨 Major Funding for Biotech Innovation 🚨 858 Therapeutics, a San Diego-based biotechnology company, just secured $50M in Series B funding! 💰 The round was led by Avidity Partners with participation from Insight Partners, Mirae Asset Global Investments Capital, and Alexandria Venture Investments. Notable existing investors like Versant Ventures, New Enterprise Associates (NEA), and Logos Capital also joined. As part of the deal, Monal Mehta, PhD, from Avidity Partners, will be joining the company’s Board of Directors. 🎯 💡 Led by CEO Jeffrey Stafford, 858 Therapeutics is developing small molecule therapeutics targeting novel oncology and immunology pathways. The company’s lead asset, ETX-19477, is a PARG inhibitor that focuses on DNA damage repair and is currently in clinical trials for patients with advanced solid tumors. 🔬 Why it Matters: PARG inhibitors like ETX-19477 represent a promising new approach in cancer treatment, targeting critical nodes in cancer biology. Data from ongoing trials will be pivotal in shaping the future of this groundbreaking therapy. 🌟 🚀 What’s next? With this fresh funding, 858 Therapeutics is set to advance its pipeline and accelerate the clinical development of ETX-19477, aiming to bring innovative cancer treatments to patients who need them most. 📢 Stay tuned to Future Techly for the latest updates on this groundbreaking initiative and the latest in tech and business! 💥 Source: FinSMEs #Biotech #CancerResearch #DrugDiscovery #SeriesBFunding #Oncology #InnovativeTherapies #FutureTechly

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Over the past year, I’ve been collecting papers with surprising results about the success of machine learning in biology, ones that run against the grain of popular conceptions, that throw into question whether our models are learning biology at all. These are critical results for scientific and technological progress. These results are reminiscent of the hoary computational chemistry wisdom: It’s easy to build models that are effective locally but not globally. When claims of excellent global performance are made they reflect small training sets, bad testing hygiene, or highly biased evaluation data Perhaps it is not Machine Learning that matters, but instead Active Learning. Operationalizing Design-Build-Test cycles requires neither omniscient nor perfectly accurate models. All it requires is that the representations used, and the models built on top of them, are faithful enough that we can make local predictions from their results that are approximately correctly ordered. If anything, these assumptions are weaker than we have traditionally thought. What do we need? “Frictionless Reproducibility.” The ability to rapidly and intelligently analyze results, pick the next set of candidates to test, and then to assay them. The transformation of processes designed for solo scientists into active-learning driven industrial discovery will demand a new set of tools and systems, most of which do not yet exist. Lots more below: https://lnkd.in/e-xX5vi7 #ai #biotech #ml #biopharma #datascience

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BacStich DNA CSO Sasha Levy, PhD, is at SynBioBeta's Synthetic Biology Conference in San Jose this week to speak on the MITRE sponsored panel "BioNet: Creating a Distributed SynBio Ecosystem for Everyone" on May 8th from 1:45-2:30. Come by and say hi! SynBioBeta MITRE #syntheticbiology #dnasynthesis

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How to differentiate hype from substance in the AI drug discovery space? A pretty cool take by Dr. Alex Zhavoronkov, co-founder and co-CEO of Insilico Medicine, published in Genetic Engineering & Biotechnology News today. Alex suggests to ask 10 simple but clear questions when evaluating AI drug discovery companies for the purpose of invstment or partnering: ✔ How many of your internally discovered drugs are in Phase I, Phase II, and Phase III? ✔ Did any of your pharma partnerships progress into Phase I, II, and III, and how long did it take? ✔ How many preclinical candidates (PCCs) did you nominate in a single year and in total? ✔ How long, on average, does it take you to nominate a PCC, and how much does it cost? ✔ What was the level of novelty of the targets and what is the level of novelty of the molecules? ✔ How long, on average, does it take you to go into clinical trials with an AI-discovered target? ✔ Did you in-license any of your drugs from other companies, repurpose them, or discover them internally? ✔ How many pharma companies use your AI software to discover drugs, and how many users do you have? How many renew every year? ✔ What was your revenue last year, and what was the revenue growth year over year? ✔ How much money have you raised, in how many rounds, and what is the valuation of your last round? What do you think? Write additional questions in the comments. I have my thoughts on it, but it is a topic for another post... #drugdiscovery #drugdesign #deeplearning Image credit: iStock

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