Nikhil Haas

A great perspective highlighting the need for an #omics or #multiomics approach. This is one reason I decided to take my 16 years of #Genomics and #NGS experience to drive an effort in scalable and affordable proteomics at Pictor .

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Wow - 2 ex-Meta employees launch EvolutionaryScale landing $142m to advance AI in biology. EvolutionaryScale has introduced ESM3, a cutting-edge language model for biology that predicts protein sequences, structures, and functions. This model can simulate millions of years of evolution, generating new proteins with potential applications in medicine, research, and sustainability. ESM3 represents a significant advancement in AI's ability to design and engineer biological systems. There will be comparisons with AlphaFold3, intrigued where this goes... https://lnkd.in/dhtQDtsA #AIinDrugDiscovery #genomics #proteomics

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The Era of 1-bit LLMs “Every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption.

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The Silent Tutors: How LLMs Are Bridging Knowledge Gaps in Cell Therapy Manufacturing

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Recently I had a great discussion on the Why, the What, and the How of Computational Sciences, broadly defined. I decided to turn that into a post! These are my personal takes on the 3 strategic directives, and borrow a lot from folks like Simon Sinek, Stephanie Wisner, Bo Bresson, and Eric Siegel, just to name a few. Hope you like them and would love to discuss more. Follow me for takes in computational biology and machine learning. Until then, happy (strategic) computing! #computationalbiology #machinelearning #strategy https://lnkd.in/eTNYNzzR

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This article is inspired by comments from Tony Czarnik and Stefan Ivanov regarding my previous blog post at https://lnkd.in/gFPhXfKW. To clarify, I am writing from the perspective of an AI believer or practitioner, acknowledging the critical viewpoint that claims "AI has shown little in terms of actual achievements in drug discovery." However, I believe it is not sufficient to simply agree with this stance without further elaboration. My focus here is on higher-level concepts rather than practical examples. Here are three key takeaways: 1. Need for Comprehensive Data Collection The current drug discovery process often lacks systematic documentation and relies heavily on subjective reporting, which hampers the ability to draw robust conclusions. Scientists, particularly chemists and biologists, should prioritize automating the collection of extensive data and metadata to create a rich database for analysis. 2. Integration of Mathematical and Data-Driven Approaches Two primary strategies for improving drug discovery are mathematical modeling and data-driven methods. While traditional scientific practices lean toward mathematical modeling for deeper understanding, advancements in AI highlight the effectiveness of data-driven approaches in addressing existing challenges and providing new insights into complex biological processes. 3. Transformative Role of AI AI technology plays a crucial role in drug discovery by enabling scientists to embrace data-driven methodologies that enhance problem-solving capabilities and deepen understanding of biological systems. Combining both mathematical models and data-driven techniques can lead to a more robust framework for innovation in the pharmaceutical industry.

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He used to be a postdoctoral research fellow at Duke University. Now as a Prinicipal Scientist at a biotech company that is orchestrating the power of the epigenome, Brian shares his thoughts on one of the most important questions of his career: "What is the most rewarding aspect of working at a biotech company??" His response below is simple but powerful! 👏🏽👏🏽👏🏽 Watch the full podcast here: https://lnkd.in/eP8ZxGrV

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Benchmarks! Yes! ~ as AI models becoming these increasingly complex multimodal and generative beasts, it’s crucial that we keep benchmarks up to date, challenging, relevant and informative.

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Lately, I've been experimenting and building data pipelines for Future Therapeutics and realized how little we talk about data pipelines in cheminformatics. It's a domain where, at any given time, we handle large volumes of data from multiple sources. I'm sharing a special collection of articles on Cheminformatics Data Pipelining, designed to raise awareness and enhance skills in this critical area. Part 1 of 8 is published. Have a look and feel free to share your feedback. Also, let me know if I miss something specific for cheminformatics. I would also love to talk to those actively involved in data pipelining projects to hear your thoughts and approaches. #cheminformatics #drugdiscovery #datapipelines https://lnkd.in/gDa7G2_z

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I’m excited to kick off The State of AI in Drug Discovery event with a Keynote Fireside Chat and 5,000+ attendees at 8:10am PT / 11:10am ET alongside Fay Lin, PhD. Together, we’ll explore how AI is transforming drug discovery end-to-end—from mapping biology to designing new chemical entities—redefining the future of pharma. I’m honored to serve alongside David Baker, who will share insights from his groundbreaking work using computers to design novel proteins. The advancements from his team at the University of Washington, Institute for Protein Design and the achievements of Demis Hassabis and John Jumper at Google DeepMind—now recognized with The Nobel Prize — have profoundly accelerated the field of protein design. At Recursion, we are leveraging multi-omic datasets—spanning phenomics, genomics, transcriptomics, and increasingly proteomics, and beyond— to create AI-powered Maps of Biology. These maps allow us to uncover novel biological insights and identify new biology and therapeutic targets that were previously out of reach. By combining this biological knowledge with cutting-edge AI-powered molecular design, we are crafting molecules that have the potential to become transformative medicines. In development, we leverage AI-powered solutions to advance predictive trial design, streamline execution, and operationalize workflows. AI helps us make smarter, faster decisions—from optimizing patient recruitment to accelerating evidence generation—improving the probability of success at every stage. Discovery and development remain profoundly humbling endeavors, reminding us of the complexity of biology. But through AI, we are unlocking new possibilities to deliver better medicines faster, ensuring treatments reach patients efficiently, with the highest levels of safety, precision, and impact. The secret to our success lies in our people and culture, who are bilingual in both the art of science and the language of AI. This unique blend allows us to navigate the complexities of biology and technology with creativity, precision, and agility. I look forward to sharing more about these innovations and continuing the conversation at the event! Details on the Oct. 30 The State of AI in Drug Discovery event here: https://lnkd.in/ec5nHGQN Read the full article in GEN here: https://lnkd.in/egEjQvtM #ai #techbio #proteins #chemistry #drugdiscovery #data #biology #maps

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According to the latest Pitchbook data, venture creation in biotech hit its slowest quarterly pace in eight years during 1Q 2024. With just over 60 new biotechs raising their first round of financing, the sector’s company formation activity has slowed 50-60% from its historic peak in 2021. Thank you Bruce Booth for penning your views on why this is a good thing. -Contraction in venture creation IS a healthy dynamic for the sector. -We need to ensure that the best ideas are getting advanced by the sector. -Greater discipline should translate into better returns in the long term for LPs and GPs/ VC #Venturecapital #Startups

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The number of submissions to arxiv is getting to be insane - almost 25k per month. Interesting that the trend started before deep learning and LLMs.

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I worked in a CRISPR lab during my bachelors and this is insane: Profluent, a Berkeley startup, has created AI that generates novel CRISPR gene editors. The AI learns from analyzing vast biological data to create editors that have never existed in nature. And for context prior to this, CRISPR methods used mechanisms found in nature to explore new variations. This breakthrough points to a future of battling disease with greater precision and speed. And even crazier, Profluent literally open-sourced one of these AI-generated editors, OpenCRISPR-1, for free experimentation. While still very early, this technology holds immense potential. And in my personal opinion, could be the potential gateway for personalized medicine. AI is accelerating progress in healthcare and pushing boundaries faster than ever before. And at this point it's inevitable whether you like it or not.. From creating new proteins for vaccines to revolutionizing gene editing, the possibilities are endless. I definitely think we're witnessing a new era of medicine unfold. Does this excite you or worry you we're gonna see something out of the X-men? Follow Scott Caputo - every week I explore AI, innovation & strategy #AI #GeneEditing #Healthcare #Biotech #Innovation

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I'm sharing with you why long reads have propelled genetic sequencing and precision medicine into a new direction. Remember when we relied purely on Excel to organize and analyze sequencing data (some companies still do this)? And now we sprinkle in AI, making long-read sequencing analysis even more efficient. Here is more of what you need to know 🔊 https://lnkd.in/dgWRbjqF #longreadsequencing #genetics #geneticists

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Bioinformatics just got 100X faster. Yesterday, I needed to build a custom differential expression pipeline for analyzing bulk RNA-seq data. In the before-times, I might have spent a few hours coding a pipeline in WDL or bash, then a few more hours wrangling dependencies, testing, and hunting down bugs. Instead, I simply wrote a description of the analysis I wanted to perform, and Shire Studio produced a working pipeline in under 5 minutes. Here's the best part: you can use this differential expression pipeline, too. Run it as-is, or modify it via simple prompting. Link in the comments :)

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If you use LLMs in your cancer research, check out my recent seminar all about how data quality impacts LLM performance. A reliable foundation that is well annotated and accessible to an LLM plays a major role in the value of its results. I share examples of how LLM-powered AI agents query across three versions of the same gene expression corpus with differing results, including: --Unstructured data from the public repository Gene Expression Omnibus. --Structured data from the Crowd Extracted Expression of Differential Signatures project (tool developed by the Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai). --Clean, linked, and harmonized data. Watch it here: https://lnkd.in/gbPB5CRv #LLM #Data #Cancer #Research #Science #Innovation

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