Machine Learning Spot’s Post

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

AlphaFold 3 Unlocks Secrets of Life’s Molecules - Revolutionary Molecular Modeling: AlphaFold 3 by Google DeepMind can predict structures and interactions of biomolecules like proteins, DNA, and RNA, enhancing drug discovery and treatment development. - Advanced AI Techniques: Features improved deep learning architectures, such as the Evoformer module and a diffusion network, boosting prediction accuracy for complex molecular interactions. - Accessible AI Technology: AlphaFold Server launched, providing free access for researchers to use AlphaFold 3 for non-commercial research, democratizing advanced molecular prediction technology. Subscribe to our daily newsletter here for more AI news https://lnkd.in/g8YaPTtq #Technology #Innovation #AI #ArtificialIntelligence #Healthcare #Research #Biotechnology #DataScience #Engineering #Science

🥇 Inside the Best Paper from the NeurIPS Foundation Models for Science Workshop. At NeurIPS, Philip Fradkin of Valence Labs powered by Recursion presented MolPhenix, a foundation model that can predict the effect of any given molecule and concentration pair on phenotypic cell assays and cell morphology. It was awarded best paper at the NeurIPS Foundation Models for Science Workshop. ▪️ As Fradkin explains, the model relies on our massive dataset of high resolution images of cells which are captured under various forms of molecular perturbation at different concentrations. ▪️ “The thing we’re interested in is called PhenoMolecular Retrieval,” he says. “To retrieve the identity of the perturbation and the concentration that was used to perturb the phenomics data” with the goal of “zero-shot generalization of new molecules at new concentrations.”  ▪️ MolPhenix is one of the first such models, he says, to integrate concentration, and that's critical to AI drug discovery. 👉 Learn more about MolPhenix here: https://lnkd.in/eU8N6NPQ

Big news in the world of science! Google DeepMind has unveiled AlphaFold 3, a groundbreaking AI capable of predicting the structure and behaviors of vital molecules such as proteins, DNA, and RNA. This breakthrough represents a giant leap forward in our capacity to comprehend and manipulate biological functions. AlphaFold 3 isn’t just a scientific milestone; it’s a gateway to vast potential. With its arrival, we stand on the brink of accelerating drug discovery, bolstering crop resilience, and more. The best part? It's freely accessible to scientists worldwide through the AlphaFold Server.🌐 Envision the possibilities as we gain the ability to forecast molecular interactions with unparalleled precision! What might this mean for future medical advancements and our understanding of various diseases? 👩💼 #Innovation #ArtificialIntelligence #Biotechnology

🔬 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

“There’s a revolution going on right now in how we use images.” Recursion’s research arm, Valence Labs, recently released a series of videos from their Machine Learning for Drug Discovery Summer School at Mila - Quebec Artificial Intelligence Institute which featured an array of experts teaching topics like protein folding, LLMs in drug design, and causal discovery. Among them is a talk on “Phenomics in Drug Discovery – Microscopy and Machine Learning” from Anne Carpenter, Senior Director of the Imaging Platform at the Broad Institute of MIT and Harvard and one of our Scientific Advisory Board members. It’s a topic of particular relevance to us, as Recursion’s earliest discovery – a new potential treatment for the devastating rare disease cerebral cavernous malformation now in Phase 2 trials – arose from work done with Carpenter’s early software for profiling cell images. In the talk, Carpenter describes why looking at phenomics – the outcome in a biological system – is such a powerful tool for drug discovery. By examining the state of human cells under various diseased and healthy conditions, it provides insight into how these changes might present in humans. And it allows us to run experiments at massive scale, vastly speeding up the discovery process at manageable cost. The process begins with multi-well plates that are equivalent to 384 tiny test tubes – each with different cells and compounds. The bottom of these plates are transparent and robot microscopes work around the clock to provide millions of images per week. Next, the images need to be analyzed using machine learning. She wrote one of the earliest software programs for this process, CellProfiler. Recursion has now developed our own machine learning foundation model called Phenom, a vision transformer utilizing hundreds of millions of parameters trained on billions of biological images from our proprietary phenomics library that we continue to scale.  While deep learning has transformed the drug discovery process, she says the remaining hurdle was generalizability – ensuring that the model can perform equally well on different datasets. Now, with Phenom and other models, that hurdle, too, has been overcome. Learn more:  👉 Anne Carpenter Phenomics in Drug Discovery: https://lnkd.in/e4gqs5RV 👉 Full ML in Drug Discovery Summer School playlist: https://lnkd.in/eAmDN-2N #ai #ml #drugdiscovery #techbio #phenomics #cells #imaging

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

🚀 The PitchBook Emerging Tech Research team's SynBioBeta Recap for Q2 2024 is live! Dive into our comprehensive report covering the latest insights and trends from SynBioBeta 2024. 🌟 Key Takeaways: 💡 AI & Digital Biology: Dominating discussions with a focus on generative AI, protein folding, and drug discovery tools like AlphaFold 3. 💊  Biopharma: Strong sector driving funding through high-impact investments, larger exits like IPOs, and M&A despite fewer deals post-pandemic. 🔬 Industrial Biotech: Facing investment struggles due to low exit activity and profitability, highlighting the need for stronger data to de-risk science. Life Science Tools & Services: Selective funding for later-stage companies with scalable, profitable products. Innovations in DNA synthesis, proteomic characterization, and single-cell genomics are critical but capital-intensive. 🌱 Bioeconomy: Focus on scalable biomanufacturing solutions and advanced life science tools to reduce costs and improve efficiency in product/drug development. 🍄 Psychedelics: Growing attention on integrating synthetic biology with psychedelic therapies, navigating unique regulatory and scientific challenges. 📅 Longevity: Rising interest in cell and gene therapies for antiaging, a field with transformative potential but long timelines and shifting regulations. #SynBio #AI #DigitalBiology #Biopharma #IndustrialBiotech #LifeScienceTools #Bioeconomy #Psychedelics #Longevity #EmergingTech #Innovation #Research

If you ever had concerns about the growing impact of AI/ML on drug discovery, take a look at the latest version of AlphaFold3. That is scientific progress with deep impact on quality, cost, timelines of drug discovery.