Turbine develops a platform called Simulated Cell™ to simulate biological experiments prior to execution, aimed at enhancing drug development efficiency and accuracy.

Turbine

DVLP is building a new infrastructure for drug development by digitizing drug assets, financing, and development workflows to accelerate the process of turning molecules into medicines.

DVLP

Primordial AI utilizes evolutionary data and artificial intelligence to discover drug targets, focusing on achieving next-generation drug development.

Primordial AI

Turbine | 5,586 followers on LinkedIn. We are building the world’s leading predictive simulation of patient biology. | Truly meaningful medicine comes from breakthroughsin deeply understanding patient biology. We are building the world’s leading predictive simulation of patient biology.

We will empower the biopharma industry by informing the right experiments that identify and validate disease driving hidden effects.

Venture Capitalist

Vishal Gulati

Building bridges: investor, executive, teacher, public/private sector advisor

Anna Marie Wagner

Founder and Managing Director, Green Shoots Consulting, LLC

Allison Robbins, CFA

Bálint Pfliegel

Current single-cell foundational models, including scGPT, can underperform compared to simpler approaches in predicting cellular responses to perturbations, highlighting significant limitations in existing benchmark datasets: 🌲 Turbine's benchmarking showed scGPT lagging behind simpler approaches like averaging training samples and Random Forest. 🐾 Models with biologically relevant features can significantly outperform scGPT. 🔻 Perturb-Seq datasets used for benchmarking are limited by low perturbation counts and lack of intra-perturbation variance. ⛓ Pseudo-bulked expression profiles outperform foundational models, suggesting little-to-no advantage in single-cell level modeling, especially in low-heterogeneity cell lines. Findings suggest revisiting benchmarking practices for more effective evaluation of post-perturbation gene expression prediction. Read more in the following preprint: https://lnkd.in/dKBGpYxr

"Hungary & #CentralEasternEurope is getting pretty good at nurturing tech startups. But any #DeepTech founder grappling with commercializing scientific breakthroughs faces too many barriers." Click to read more from our CEO Szabolcs Nagy !

Our dearest Anna Török-Böröcz has just secured her place among the top 🇭🇺 HR leaders of year 2024! 🎊

As elegant as ADC-based therapies promise to be for oncology, as hard it is to deliver them. Our obstacles range from the very depth of biological understanding to drug design. Turbine's simulations help in: 🧬 Identifying drivers of response for payloads to guide rational patient selection 🧪 Finding the right payloads for disease-subtypes to address ADC-resistance in the clinic Our team is visiting World ADC Summit in San Diego, presenting a poster during today's scientific session. Please see our poster by clicking the link below and feel free to reach out through meetme@turbine.ai ! https://buff.ly/3UFoyR3

It's not the first time we talk about the biases in training data that lead AI disease models astray. In this podcast episode we discuss the methodology we use to accurately assess computational model performance. We also share insights from our big pharma collaboration on how we use public, internal, and partner datasets to create patient avatars for simulations. Tune in to listen or visit benchmark.turbine.ai to learn more about the EFFECT Benchmark Suite! Let's work on finding ways to make more sense of ground truth data for better models of disease biology!

We believe that RNA sequencing (RNAseq) is the best data source currently to computationally model cell behavior. Our CTO, Kristóf Szalay explores this topic in his latest blog post. 💡 Key takeaways: 1️⃣ RNAseq is cost-effective and scalable, making it ideal for large datasets with multiple perturbations. 2️⃣ Baseline RNAseq data correlates well with proteomics. 3️⃣ Basic machine learning models achieve comparable performance when predicting post-perturbation cell viability using either RNAseq or proteomics features. 4️⃣ With the right approach, downstream gene expression reveals crucial insights about upstream signaling pathways. Dive into the details by clicking the link below!

What if you could simulate experiments using patient-representative avatars and predict therapy response before testing in the wet lab? Let’s join forces to reengineer R&D! 🚀 https://lnkd.in/dzFKzR4B https://lnkd.in/d3r3898E

🔧 Kristóf Szalay was briefly interested in tanks (the scary kind), while working on this blog post. 🤖 Although we didn't put more research into the tanks themselves, there is definitely more depth to how we measure computational model predictivity for biology using our EFFECT benchmark suite on bioRxiv. Visit turbine.ai/benchmark or click here to read the preprint: https://lnkd.in/gB-CxQgy Click the link below to read the blogpost

"The right place, at the right time", or so we have hoped to sigh in relief ever since founding Turbine in 2016. Although the right time seemed earlier for us, with 2 Nobel Prizes already awarded in 2024 for complex computational advances in science, it's becoming safe to say that we are heard more and more. What does "we" mean in the sentence above? There is no comprehensive list, but here is an example to consider: 80 companies, including Turbine, all just awarded six-digit USD commitments to lead the charge. Congratulations to the Nobel Prize Winners (John J. Hopfield, Geoffrey E. Hinton, David Baker, Demis Hassabis and John M. Jumper) and to the Amazon Web Services (AWS) AI Accelerator cohort!

🎉 Szabolcs Nagy has been named one of the top 50 innovators of a 100 most influential AI professionals in Europe by l'Opinion , the influential French daily. The article recognized our work in bringing the prowess of Simulated Cell™ to the biopharma industry. Check out the profile to learn more about Szabi’s thoughts on the future of AI in Europe, as well as our killer team's impact - pardon our French!

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Company Name	Turbine	DVLP	Primordial AI
Symbol
MSH ID	AI:TURBINE	COM:DVLPMEDICINES	COM:PRIMORDIALBIO
Market	PRIVATE	PRIVATE	PRIVATE
Sector	Healthcare & Health Services	Healthcare & Health Services	Healthcare & Health Services
Industry	Biotechnology	Pharmaceuticals	Biotechnology
Country	US	US	US
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Website	turbine.ai	dvlpmedicines.com	primordialbio.com
LinkedIn	turbine-ai	dvlpmedicines	primordial-ai
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Monthly Web Traffic	2.60K
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Turbine

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