Silica Corpora’s Post

Check what Silica Corpora can do for your antibody design project 👇👇👇

𝐀𝐝𝐝𝐫𝐞𝐬𝐬𝐢𝐧𝐠 𝐋𝐚𝐫𝐠𝐞 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞𝐬' 𝐀𝐃𝐌𝐄 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐰𝐢𝐭𝐡 𝐡𝐲𝐛𝐫𝐢𝐝 𝐀𝐈 At VeriSIM Life we empathize with the challenges faced in the journey of drug discovery, particularly when it involves large molecules like proteins, antibodies, and nucleic acids. These molecules, integral to advancing medical treatments, present a unique challenge compared to their smaller counterparts. The intricacy of their structures and interactions within the human body makes traditional high-throughput screening methods, which are effective for small molecules, less suitable. This is because high-throughput techniques struggle to accurately capture the complex behaviors and interactions of large molecules in the diverse and dynamic biological environment. This limitation significantly complicates the understanding of their absorption, distribution, metabolism, and excretion (ADME) properties. In response to this challenge, VeriSIMLife has innovated a hybrid approach, merging the analytical power of artificial intelligence (AI) with deep scientific knowledge. Our AI techniques, including machine learning and deep learning, are adept at handling the complexities inherent to large molecules such as their higher degree of structural variability, extensive post-translational modifications, and complex 3D conformations. By analyzing extensive datasets, these models uncover patterns and features critical to understanding a molecule's #ADME characteristics. In a recent client project focused on classifying antibodies based on their magnitude of clearance from the body, VeriSIMLife employed Hybrid AI-driven feature transformation and analysis with #BIOiSIM, focusing on key chemical descriptors and leveraging physiological features. This approach enabled us to identify the most sensitive parameters, leading to a 21% improvement in model accuracy over the existing framework. Our method effectively harnessed factors related to the molecules’ interaction and compatibility within biological systems, demonstrating our proficiency in handling complex molecular data with efficiency and precision. Our mission is not only to tackle these scientific challenges but also to do so with a deep sense of responsibility and compassion towards the scientific community and the patients who stand to benefit from these therapies. By enhancing our AI-driven approach with mathematical modeling, we provide a more holistic understanding of a molecule's journey through the body. This comprehensive strategy not only accelerates the drug development process but also aligns with our commitment to improving human health. We are dedicated to transforming the landscape of large molecule drug development, ensuring that each step we take brings us closer to delivering effective and life-changing treatments to those in need.

⚡ Imagine a future where AI not only predicts protein structures but also designs new drugs faster than ever. AlphaFold3 is making that future a reality. ⚡ 🚀 AlphaFold3 paves the way for faster, more cost-effective virtual screening by integrating protein, nucleic acid, ion, and ligand predictions, enabling concurrent 3D docking, and reducing reliance on pre-existing molecular structures. 👉 Discover how this groundbreaking technology might shape the future of pharma and biotech in our blog post: https://lnkd.in/dMmANpEb   #Bioinformatics #Biotechnology #Pharma #ai

Join us for an in-depth webinar on "Maximizing Bioproduction with CELLiST: Leveraging Multiomics and AI for Superior Cell Culture Media." Discover how cutting-edge technologies can optimize your cell culture media to enhance protein yield and overall production efficiency. Don't miss this opportunity to gain insights from industry leaders and take your bioproduction processes to the next level. Achieve unparalleled results in your cell culture processes with CELLiST BASAL CHO MX Medium. Developed through a collaboration between KBI Biopharma and JSR Life Sciences, this medium ensures optimal cell growth and maximum productivity with its chemically defined, animal-origin free formula. Whether you're working with CHO-M, CHO-GS, or other CHO cell lines, discover how this medium can elevate your bioproduction to new heights.   Full recorded webinars : https://lnkd.in/gNSqphDE   #CELLiST #AjinomotoCELLiSTKorea #JSRLifeSciences #Webinar

📈 In silico research is a computational approach to understanding complex biological processes and data, at APIS we utilise AI and ML for deep analysis and insightful reporting. 💡To make informed and rapid decisions, you require dependable data and reliable technology. 🧬 APIS In Silico Research can help you achieve: ·   Accelerated biomarker discovery ·   Streamlined early drug discovery ·   More precise molecular diagnostics ·   Protein interaction network discovery ·   Aptamer and small molecule drug analogue discovery Dive into the future of biomarker identification! 🔬 https://lnkd.in/eme2m-ZY   #Bioinformatics #AI #ML #Insilico #DataAnalysis #Biomarker 

AI discovers the first new antibiotic in over 60 years AI has made a breakthrough in the field of medicine by discovering a powerful new antibiotic, the first of its kind in over six decades. Utilizing a machine-learning algorithm, researchers trained the AI using a digital library of several thousand molecules, including one that was ultimately identified as having strong antibacterial properties without being toxic to human cells. The new drug, named halicin after the AI system HAL from the movie "2001: A Space Odyssey," was tested against dozens of bacterial strains and found to be effective against a wide spectrum of microbes, including antibiotic-resistant species. The AI ignored traditional antibiotic compounds, which are often structurally similar, and instead selected molecules with distinctive features and mechanisms. Halicin works by disrupting the bacteria's ability to maintain an electrochemical gradient across their cell membranes, which is essential for their survival. Unlike other antibiotics that target specific proteins, which bacteria can evolve to resist, halicin's general mechanism makes it much harder for bacteria to develop resistance. This groundbreaking discovery suggests that AI could revolutionize the way new medicines are discovered, providing a powerful tool against antibiotic resistance which is a growing threat to global health. Read Full Article Here: https://lnkd.in/dAJ6zvVS #AntibioticResistance #Halicin #AIMedicine #DrugDiscovery #MachineLearning #HealthcareInnovation #Antibacterial #MedicalResearch #BioTech #Pharmaceuticals

Qgen Health Lab offers a cutting-edge web-based application leveraged for drug discovery and genomics applications. This platform aids in: Identification of Drug Repurposing Opportunities ✔️Understanding Mechanisms of Action ✔️Prediction of Off-target Effects and Side Effects etc. ✔️The app yields an easy to digest dashboard that helps to analyze molecular targets as proteins, kinases, genes that can be antagonized with various drugs. It also provides users to have a deeper look into detailed drug profiles, pathways and interaction with various molecular targets. The analysis is particularly convenient due to simplicity, accessibility, user controls etc. Users frequently claimed that despite harboring humongous amount of data, the interface helps to reduce cognitive load thus making it a pleasure to work in. #healthcaredesign #uxui #patientcare #medicalinnovation #userexperience

Thrilled to announce that our paper on Drug-Target Interaction (DTI) prediction has been accepted at RECOMB 2024, following its acceptance at the NeurIPS 2023 AI for Drug Discovery and Development Workshop! 📄 FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation Abstract: Drug-Target Interaction (DTI) prediction is crucial for drug discovery, and I'm proud to introduce our novel transformer-based model, FragXsiteDTI. This groundbreaking model is the first to leverage both drug molecule fragments and protein pockets simultaneously, providing a detailed perspective on their interaction. 🧠 Key Features: - Inspired by the Perceiver IO framework - Learnable latent array for seamless information translation - Cross-attention between protein binding site embeddings and drug fragments - Superior predictive power demonstrated on benchmarking datasets - Model interpretability showcased on critical components of drug molecules and target proteins 🔗 arXiv Link: https://lnkd.in/e5mD-AMw 🔗 NeurIPS Poster: https://lnkd.in/e9NdYG3i Excited to present this work at RECOMB 2024 and looking forward to the discussions it sparks! Grateful for the incredible team and collaborators who made this possible. #DrugDiscovery #fragXsiteDTI #Bioinformatics #RECOMB #NeurIPS #Research #MachineLearning #AI #Science #interpretability

Miss us at PEGS? Find out how Tamarack Bioscience, Inc. used OpenProtein.AI to engineer novel reverse transcriptase polymerase with more than 10 amino acid substitutions at once. In a collaboration between TamarackBio and OpenProtein.AI, scientists at Tamarack Bio were able to leverage OpenProtein.AI’s protein design tools to design 5 new polymerase variants for reverse transcription. These enzymes were critical COVID PCR testing and remain an important workhorse in diagnostics development and as laboratory reagents. By using machine-learning guided approaches to engineering proteins, the team was able to quickly design high activity, high diversity variants for screening. The designed variants had around 17 amino acid substitutions each and displayed high activity. This greatly reduced the time and resources used in screening for novel enzyme variants! Be sure to check out how you can apply the OpenProtein.AI platform to your protein engineering data by contacting us at contact@openprotein.ai. Or, sign up for early access now https://lnkd.in/gMmKKCfQ  #PEGS #openproteinai #machinelearning #reversetranscription #pcr #polymerase #proteinengineering #enzymeengineering

Exciting news! Creyon has been granted another U.S. patent for our AI/machine learning-enabled engineering of therapeutic oligonucleotides. What it covers: Our proprietary method for training machine-learned models to optimize oligonucleotide designs for desired biophysical effects and pharmacological endpoints. Why it matters: This technology allows us to create the data and the models that underlie our ability to engineer potential therapies and improve lead identification efficiency by up to 100-fold over industry peers. What's next: Accelerated development of life-changing treatments for both rare and common diseases. Read our press release to learn more about how we're revolutionizing drug development: https://lnkd.in/e64DYGBM #FutureOfMedicine #AIinHealthcare