Terray Therapeutics’ Post

Using machine learning to discover the next generation of permanent magnets!🔍💡 This study identifies promising Fe–Co–C compounds with high magnetization and magnetic anisotropy—key for rare earth-free magnets. ML is revolutionizing the search for materials with tailored properties for advanced tech!🔬 https://lnkd.in/ghziKt4i Visit www.epicbnnt.com🔎 Call us at (410) 967-0557📞 Email us at [email protected]✉️ Follow us on: LinkedIn @Epic Advanced Materials Instagram @epicnanotech #Nano #BNNTs #ImagineNano #BoronNitrideNanotubes #Nanotubes #MachineLearning #Nanotechnology #DeepLearning #MaterialDesign #Nanomaterials #EPIC #EpicNano #EpicNanoTech #EpicBnnt #EpicAdvancedMaterials #DataScience #Tech #ImpactLearning #ML #BigData #Nanotech #NanotechBNNT #Science #Pharmaceuticals #Technology #MaterialScience #AI #Innovation #Sustainability #AdvancedManufacturing #Futurism #Creativity #ScienceandTechnology #ThermalManagement #HealthTech #HeatTransfer #NanoscaleScience #TechRevolution #FutureScience #MaterialsScience #MagnetTechnology #CleanEnergy #SmartMaterials

Scientists confirm the existence of π-hole in molecules, proving decades-old theory Scientists used advanced scanning electron microscopy to image the inside of a molecule and the structure of the electron shell of the atom. They confirmed the non-uniform distribution of electron density in aromatic molecules and the existence of the π-hole. Using the Kelvin Probe Force Microscopy (KFPM), they have observed the π-hole (π-electron-deficient cavity in halogen-substituted polyaromatic hydrocarbon compounds) on a single molecule. And now, you can see the π-hole too. The picture below shows from left to right the chemical structure of the 9,10-Dichlorooctafluoroanthracene (FCl-An) molecule, the calculated electrostatic potential map of the molecule, the experimental KPFM image, and the simulated KPFM image. The presence of a positive charge in the central part of the FCl-An molecule is also indirectly supported by the good agreement between the experimental and simulated patterns shown in the image. The discovery is significant at the submolecular level, comparable to that of cosmic black holes. This phenomenon significantly affects the physicochemical properties of molecules and their interactions. A better understanding of electron charge distribution will aid in building new supramolecules and developing advanced nanomaterials. Credit: https://lnkd.in/d75KKC7v Read More: https://lnkd.in/dGhtTc5p ================================================ Why you are seeing this post I share these insights on LinkedIn to inspire and empower others with knowledge meticulously curated from the finest sources.  My goal is to spark ideas, foster innovation, and connect with those who may need support in overcoming their own technological hurdles. If you're navigating a challenge or looking for ways to boost your business, let’s connect and DM me—I'm here to help. What I do I provide consultancy services for the entire product development journey, from ideation to sales. If you are facing an innovation challenge, dealing with an engineering problem, or founding a startup, especially in but not limited to all chemical/material-related industries, I can assist you.

Artificial and living cells coexist and interact with each other - sounds like science fiction? 👾 🤖 That's what the recently launched Bio-HhOST project is all about. 😁 Learn here what the MIC and microfluidics have to do with it 😋 : https://lnkd.in/eKi-KbVb #microfluidics #innovation #scientificinnovation #collaboration

Light to control Biology. Prolific Machines, Inc. recently unveiled their photomolecular platform that controls cellular behavior using ... light! After diving a bit deeper into the science behind it, I found: The light can affect cellular processes using one basic principle called induced dimerization — when two proteins are brought together upon illumination. One of the methods is optogenetic dimerization: photosensitive proteins change their shape upon absorption of the photon, leading to protein activation. For example, Zdk (x) protein binds to the LOV2 (y) domain in the dark and dissociates upon light exposure. The chemo-optogenetic system controls the dimerization (joining of two molecules) of proteins or other biomolecules that contain a 🔐 cage group that prevents their activity until it’s removed by specific stimuli (light 🔑). We can say that light makes proteins start playing around in the cell, interacting, and activating pathways!! What if cells could be self-sufficient by expressing growth factors themselves using light ⚡?? https://lnkd.in/d9akRpkP

PocketGen: Deep generative modeling for protein pocket design. Designing protein pockets capable of binding specific ligands with high affinity is a foundational challenge in drug discovery and enzyme engineering. The complexity of protein-ligand interactions, the flexibility of molecular structures, and the intricate dependencies between sequence and structure have long posed significant obstacles. In response to these challenges, a recent study led by Zaixi Zhang introduces PocketGen, a deep generative model developed to efficiently create high-affinity protein pockets. By combining advanced graph transformers and protein language models, PocketGen marks a significant step forward in computational protein design. PocketGen employs a bilevel graph transformer to model atomic and residue-level interactions within protein-ligand complexes. This architecture enables the model to capture the intricate dynamics of both intraprotein and protein-ligand interactions while maintaining alignment between the generated sequence and its structural context. To refine protein sequences, PocketGen integrates a structural adapter within a pre-trained protein language model, allowing sequence predictions to align with structural features. This approach ensures that sequence and structure co-evolve seamlessly during the generation process, addressing limitations in prior methods that treat these aspects independently. The performance of PocketGen underscores its potential. Benchmarking on datasets such as CrossDocked and Binding MOAD demonstrates its ability to surpass existing methods in both structural accuracy and binding affinity. PocketGen achieves a 97% success rate in generating protein pockets with higher binding affinity than reference structures and an amino acid recovery rate exceeding 63%. Additionally, it operates approximately ten times faster than traditional physics-based methods, which are often computationally intensive. Paper: https://lnkd.in/dnP3jXpn Preprint: https://lnkd.in/dtnWy3yF #ProteinDesign #AIforBiotech #AIforScience #DeepLearning #DrugDiscovery #ComputationalBiology #GenerativeAI #EnzymeEngineering #MachineLearning #StructuralBiology #Bioinformatics #AIinHealthcare #DrugDevelopment #Biotechnology #AIApplications #InnovationInBiotech

Glad to share our latest publication: "Integration of spectroscopic techniques and machine learning for optimizing Phaeodactylum tricornutum cell and fucoxanthin productivity." This work explores how spectroscopy combined with machine learning can drive innovation in optimizing microalgal productivity—paving the way for more sustainable and efficient bioprocesses. Check out the paper here: https://lnkd.in/d94Y9WhN Feel free to reach out or comment if this topic resonates with your interests! #MachineLearning #Bioprocessing #Spectroscopy #Microalgae

Sapio Sciences' latest release, 2412, introduces cutting-edge tools tailored for chemistry, molecular biology, and more. Imagine seamless collaboration, advanced data insights, and streamlined operations—all in one platform. Check it out: https://bit.ly/4g67U4Y 💬 What feature would make the biggest difference in your lab?

While there are numerous numerical models exploring various nanofluids in different microchannel heat sink geometries under various flow conditions, only a few incorporate experimental data. Existing reports generally suggest the cooling potential of nanofluids, but none have specifically investigated the effects of the surfactant alone on fluid flow, particularly regarding the thermofluid flow pattern🔬 https://lnkd.in/g8qA7aQC Visit www.epicbnnt.com🔎 Call us at (410) 967-0557📞 Email us at [email protected]✉️ Follow us on: LinkedIn @Epic Advanced Materials Instagram @epicnanotech #nano #bnnts #imaginenano #boronnitridenanotubes #nanotubes #machinelearning #nanotechnology #deeplearning #materialdesign #nanomaterials #EPIC #EpicNano #EpicNanoTech #EpicBnnt #EpicAdvancedMaterials #nanotech #nanotechbnnt #science #pharmaceuticals #technology #materialscience #ai #innovation #sustainability #advancedmanufacturing #futurism #creativity #scienceandtechnology

📣 Join the talk and get updated on recent innovations in the field of particle technology.

🚀 𝐀𝐝𝐯𝐚𝐧𝐜𝐢𝐧𝐠 𝐃𝐫𝐮𝐠-𝐓𝐚𝐫𝐠𝐞𝐭 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐊𝐨𝐦𝐞𝐭 𝐚𝐧𝐝 𝐋𝐂𝐈𝐝𝐛 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 Drug discovery is entering a new era with the Komet Algorithm and the LCIdb Dataset! Designed to tackle large-scale drug-target interactions (DTIs), these innovations aim to streamline the drug development process and improve predictive accuracy. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: 🔬 LCIdb Dataset: A comprehensive DTI dataset with 271,180 molecules and 396,798 positive interactions, offering unparalleled coverage of chemical and biological spaces. This dataset enables robust machine learning training, ensuring high-quality predictions. ⚡ Komet Algorithm: Leveraging the power of the Kronecker Optimized Method, Komet is scalable and efficient. It employs: 1️⃣ GPU-parallel computation for speed. 2️⃣ Feature encoding using the Nyström approximation to reduce memory usage. 3️⃣ Tensor products to capture intricate molecule-protein interactions. 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: 1️⃣ Improved Predictions: Achieving state-of-the-art performance on both medium-sized and large datasets like DrugBank and LCIdb. 2️⃣ Scalability: Tackles computational challenges head-on, enabling predictions across vast chemical and protein spaces. 3️⃣ Real-World Impact: Demonstrates success in scaffold hopping tasks, critical for identifying novel drug candidates. 𝐀 𝐋𝐞𝐚𝐩 𝐅𝐨𝐫𝐰𝐚𝐫𝐝: With applications ranging from target de-orphanization to off-target identification, Komet and LCIdb mark a major step forward in computational chemogenomics. 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐍𝐨𝐰: 1️⃣ LCIdb and Komet Documentation 👉 https://lnkd.in/dkndinGh 2️⃣ Dataset: Zenodo Repository 👉 https://lnkd.in/dkvziSA5 Let’s shape the future of drug discovery! 🚀 Follow Medvolt for more #DrugDiscovery #AIInHealthcare #MachineLearning #Biotechnology #Innovation #KometAlgorithm #Chemogenomics