Apoha’s Post

🔬 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐞𝐞𝐭𝐬 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐁𝐢𝐨𝐥𝐨𝐠𝐲: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐀𝐜𝐜𝐞𝐥 𝐃𝐍𝐀 As Boltz-1 accelerates molecular interaction predictions, Accel DNA stands at the forefront of applying advanced AI-driven solutions to drug discovery and molecular research challenges. Accel DNA leverages cutting-edge computational techniques to analyze genetic and molecular data with unparalleled speed and precision. 🌟 𝐖𝐡𝐲 𝐀𝐜𝐜𝐞𝐥 𝐃𝐍𝐀 𝐢𝐬 𝐘𝐨𝐮𝐫 𝐏𝐚𝐫𝐭𝐧𝐞𝐫 𝐢𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: ➡AI at Scale: Integrates with platforms like Boltz-1 to provide scalable molecular modeling and drug research solutions. ➡Data-Driven Discovery: Uses robust algorithms to decode complex genetic patterns, advancing personalized medicine. ➡Open Collaboration: Embraces open-source advancements to enhance collaboration and innovation globally. 🔬 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫: Accel DNA’s tools are crafted to complement breakthroughs like Boltz-1, providing researchers with a comprehensive toolkit to accelerate insights in drug development and disease mechanisms. 📢 Join the revolution with Accel DNA—where AI meets molecular biology to transform the future of health and medicine! #AI #AccelDNA #MolecularBiology #DrugDiscovery #Innovation

People should be talking more about this. Yesterday was the release of DeepMind's latest model, AlphaProteo. This AI tool is pushing the boundaries of protein design, creating entirely new proteins with real potential to revolutionize drug discovery, disease research, and protein binding. It's still early days, but the impact is probably huge—speeding up breakthroughs and opening new possibilities in biology. It is remarkable how we see big breakthroughs in the field every few months. And above all, all the models and science behind them are open source. Read the article and access the paper: https://lnkd.in/eN35viXM

📚 Exciting News! 📚 I am thrilled to announce that I have contributed a chapter to the newly published book titled “Genomic Intelligence: Metagenomics and Artificial Intelligence.” My chapter, “AI-Powered Predictive Modelling for Disease Diagnostics,” delves into the transformative potential of artificial intelligence in the field of healthcare. In this chapter, I explore how AI-driven predictive models can revolutionize disease diagnostics, offering unprecedented accuracy and efficiency. By integrating genomic data with advanced machine learning algorithms, we can pave the way for early detection and personalized treatment plans, ultimately improving patient outcomes. For those interested in the intersection of genomics and artificial intelligence, this book is a must-read! 📖 #GenomicIntelligence #ArtificialIntelligence #DiseaseDiagnostics #PredictiveModelling #HealthcareInnovation #BookPublication

GPCR Conformational Features (Structural Aspects) Of late I haven't posted anything scientific in here. Still, I would like to start writing about some of my observations on topics which excite me as a trained scientist. To begin with let me put up in structured form the observations derived from structural studies of different classes of GPCRs. These have been taken and compiled from various sources (papers and reviews). I would be transiting to substack to cater to serious and subject matter experts interested in exploring and collaborating in the space of computational biology, AI and Generative AI. 🤝 G protein-coupled receptors (GPCRs) are classified into six main classes based on structural and functional characteristics. Each class exhibits unique conformational features influenced by specific amino acids that play crucial roles in ligand binding and receptor activation. Understanding these differences is vital for drug design and therapeutic targeting(https://lnkd.in/gWWgbhJS, https://lnkd.in/g4FXpCDg). A summary of these interactions is provided below. #gpcr #conformation #features #aminoacids #mechanismofaction #gprotein

Multimodal Foundational Models: A New Era in Biology? Foundational models have become a cornerstone in machine learning, where large models are trained on vast datasets in a self-supervised manner. These models can perform a variety of new tasks without specific training for each task. While this concept is well-known in image and text processing, it is now gaining traction in biology. Traditionally, biological models have focused on a single type of data, such as protein sequences for structure prediction. However, recent developments have introduced multimodal models that integrate various data types, leading to groundbreaking advancements. Here are some notable examples: - AlphaFold 3: This model not only predicts protein structures but also bio-molecular interactions and complexes. It can incorporate ligands, nucleic acids, and other inputs in addition to protein sequences. - LLaVA-med: By processing both images and text, LLaVA-med provides more accurate answers for medical imaging questions, leveraging the power of multimodality. - scGPT: This model classifies cell types and predicts the effects of gene perturbations. It takes gene expression data and cellular conditions as inputs, enhancing its predictive capabilities. These emerging trends suggest that we may be heading towards robust multimodal models capable of modeling entire biological systems. Imagine models that can integrate images, text, and protein sequences to make superhuman predictions from diverse inputs. This direction raises several intriguing questions: - How will these models be trained and fine-tuned? - Can they achieve a comprehensive understanding of biological processes? - What new insights and applications will they unlock? I look forward to seeing how these advancements unfold and their impact on the future of biological research and healthcare. We are dicussing our efforts to organize the world of foundational models in bilogy in an upcomming webinar - you are welcome to join: https://lnkd.in/dgXARbRm 👉 What do you think? Let’s discuss!

🧬 𝐎𝐩𝐞𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐁𝐢𝐨𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 Sometimes, groundbreaking progress comes from open collaboration—and Boltz-1 exemplifies that. This open-source model rivals AlphaFold3 in predicting the 3D structures of proteins, RNA, DNA, and small molecules, marking a turning point in molecular research. 𝐖𝐡𝐲 𝐁𝐨𝐥𝐭𝐳-𝟏 𝐒𝐭𝐚𝐧𝐝𝐬 𝐎𝐮𝐭: ➡Innovation Beyond AlphaFold3: With enhanced motion alignment via the Kabsch algorithm and streamlined training techniques, Boltz-1 achieves faster and equally accurate results. ➡Drug Discovery Impact: It excels in predicting protein-ligand interactions, a critical component for drug development. ➡Community-Driven: Unlike closed systems, Boltz-1 is free and open-source, empowering researchers worldwide to collaborate and innovate without restrictions. The open nature of Boltz-1 is a testament to the power of shared knowledge—unlocking possibilities in drug discovery, disease understanding, and biomolecular research. At Accel DNA, we take pride in leveraging cutting-edge tools like Boltz-1 to redefine possibilities in molecular biology and AI. By integrating these advancements, we’re driving the future of health and innovation forward. #AccelDNA #AI #BiomolecularInnovation #DrugDiscovery #OpenSource

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

💡 Our latest newsletter on the latest tools in Life Science that are gonna make a wave is out! As always thanks Frédéric Célerse, PhD for the help! 🚀 📌 RhoDesign: RNA Aptamer design 🧬 (thanks to Samee Ullah for posting this!) 📌 BALM - Protein-Ligand binding affinity prediction 🪢 (Rohan Gorantla) 📌 CyanobacteriaDB: Cyanobacteria Bioactive Compounds Database for Drug Development 💿 (Vitor Vasconcelos Diogo Pratas Sérgio Filipe Sousa João Carneiro) Follow Kiin AI and Subscribe to our Substack for weekly news!

𝐈𝐃𝐋 𝐌𝐮𝐥𝐭𝐢-𝐎𝐌𝐈𝐂𝐒 𝐃𝐚𝐲: 🌟 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻 𝗖𝗲𝗹𝗹 𝗔𝘁𝗹𝗮𝘀 (𝗛𝗖𝗔) 🌟 The Human Cell Atlas, an ambitious global project launched in 2016, aims to catalog every cell type in the human body. Recently, the project has reached a critical milestone, showcasing significant advancements in single-cell technologies and computational tools as highlighted in a recent #Nature article (20 November 2025). This ambitious initiative aims to create comprehensive reference maps of all human cells, which is crucial for understanding #health and #disease at a cellular level. 🔑 𝗞𝗲𝘆 𝘁𝗼 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 New computational tools that enhance our ability to analyze and interpret complex cellular data. These tools are particularly transformative in the realm of #omics, which involves the collective technologies used to explore the roles, relationships, and actions of the various types of molecules that make up the cells of an organism.    📍 𝗦𝗶𝗻𝗴𝗹𝗲-𝗖𝗲𝗹𝗹 𝗢𝗺𝗶𝗰𝘀: The HCA project is leveraging single-cell omics to provide unprecedented insights into cellular diversity and function.    📍 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗼𝗼𝗹𝘀: Innovations such as #popV and #SCHAF are at the forefront, utilizing #AI and machine learning (#ML) to automate cell annotation and generate spatial data. These tools address challenges in data resolution and cost, making it easier to integrate and analyze large-scale omics data.    📍 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗠𝗲𝗱𝗶𝗰𝗶𝗻𝗲: By improving our understanding of cellular heterogeneity and dynamics, these advancements in omics are poised to drive breakthroughs in personalized medicine, disease modeling, and drug discovery. The 𝗛𝗖𝗔 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 is not just a scientific endeavor but a collaborative effort that promises to transform our understanding of #biology and #medicine. As these tools and technologies continue to evolve, they will undoubtedly unlock new possibilities in the study of life at the cellular level. 📚 Read more interesting details here: https://lnkd.in/dGmTcZYD And here https://lnkd.in/eCNeb7-Y 🌐 Human Atlas Website: https://lnkd.in/eJUeet4V 👉 For more bioinformatics updates, follow us at Independent Data Lab Compiled by: Hassiba Belahbib #HumanCellAtlas #Biotechnology #AI #MachineLearning #Innovation #multiomics

Virtual Screening: Revolutionizing Target and Lead Discovery At Moldrug AI Systems, we specialize in virtual screening, an optimal in silico approach for modeling molecular interactions in target and lead discovery processes. These computational techniques provide valuable data about the binding of two structures (ligand and target), offering insights into the possible mechanisms of action of biomolecules related to various diseases. Our docking capabilities include: 🔹 Analysis of diverse biomolecules from different species. 🔹 Detailed information on interactions such as small molecules-protein, protein-protein, small molecules-DNA/RNA, and protein-DNA/RNA. With virtual screening, we can efficiently explore molecular interactions, aiding in the discovery of new targets and leads for therapeutic development. #MoldrugAISystems #VirtualScreening #MolecularInteractions #DrugDiscovery #Bioinformatics #InSilico #HealthcareInnovation #ScientificResearch