FutureGRIN NextGen

At the heart of drug discovery lies the need to understand molecular behaviour at the quantum level. Density Functional Theory (DFT) analysis allows us to explore molecular properties at the quantum level, providing critical insights into drug interactions, stability, and reactivity. Now, imagine combining this with AI—faster calculations, deeper insights, and unprecedented efficiency in drug design. The future of precision medicine is unfolding before our eyes! With DFT, you can predict the electronic structure of molecules. At FutureGRIN NextGen, we integrate DFT to optimize drug design and uncover novel therapeutic solutions. If AI and quantum chemistry could solve one mystery in medicine today, what would you want it to be? Let’s discuss this in the comments! ?? #densityfunctionaltheory #DFT #drugdiscovery #artificialintelligence #healthcare #pharmaceutical #quantumchemistry

Imagine sifting through millions of compounds in record time to find the perfect match for a disease target. That’s the power of virtual screening. Virtual screening isn’t just a step in drug discovery—it’s a game-changer. Do you know the interesting thing? Gone are the days of screening thousands of compounds manually. Today, virtual screening, powered by machine learning, is helping scientists rapidly identify promising drug candidates with higher precision and efficiency. From structure-based to ligand-based screening, AI is optimizing the search for the next breakthrough drug! At FutureGRIN NextGen, we combine ML and advanced algorithms to accelerate the identification of promising drug candidates, saving time and resources in the process. What’s one disease you hope AI helps us conquer in the next decade? Let’s hear your thoughts! ?? #DrugDiscovery #AI #VirtualScreening #PharmaTech #InnovationInHealthcare #Healthcare #Pharmaceutical

Molecular docking is more than just a computational tool—it's the bridge between molecules and breakthroughs. It shows how a molecule/drug interacts with the protein. By simulating how potential drug compounds interact with target proteins, we’re unlocking new possibilities in drug discovery. Aside from helping us learn the interactions, it has been widely used in ranking which molecules are more promising among arrays of choice. It has made drug discovery more fun. Many software have been developed for molecular docking, such as: 1??AutoDock and AutoDock Vina by Scripps Research 2??DockThor by the Grupo de Modelagem Molecular em Sistemas Biológicos (Molecular Modeling Group of Biological Systems) at LNCC 3??GOLD by CCDC - The Cambridge Crystallographic Data Centre 4??FlexX by BioSolveIT 5??Molegro Virtual Docker by Dassault Systèmes BIOVIA 6??Discovery Studio by Molexus At FutureGRIN NextGen, we leverage docking in many of our research, enhancing the speed of predicting potential drug candidates. Have you used molecular docking before? Let’s discuss! ?? #DrugDiscovery #AI #MolecularDocking #InnovationInHealthcare #Healthcare #ComputationalBiology #Pharmaceutical

Some 20 terms in AI-driven drug discovery lately. Which one are you hearing for the first time? #AI #drugdiscovery #terms

Debunking the Myth: AI Will Replace Drug Scientists… There's a common misconception that artificial intelligence (AI) will replace drug scientists in the development of life-saving medications. But the truth is, AI isn't here to replace scientists – it's here to assist them. AI technologies are revolutionizing the drug development process, helping scientists analyze vast datasets, predict molecular interactions, and even speed up clinical trials. However, the human element – critical thinking, creativity, ethical decision-making, and expertise – remains essential in drug discovery. In short, AI cannot, rather, will enhance the work of drug scientists, empowering them to make more informed decisions and push the boundaries of medical advancements faster than ever before. #AI #DrugDiscovery #Pharma #Innovation #Science #Technology #AIinPharma

Rational drug design is hard, rational design of targeted protein degraders, especially molecular glues, is harder. A good degrader must stabilize a ternary complex between the ligase, degrader, and target. This complex often forms a new, flexible protein-protein interface. It must adopt shapes that allow efficient ubiquitin transfer and hold together long enough for adequate ubiquitination. Can we design molecular glue degraders for specific targets? In a new manuscript, we introduce GlueMap. This computational platform combines structural, thermodynamic, and pharmacodynamic modeling to find new molecular glues, provide mechanistic insights into their activity, and guide their design and optimization. We’ve used GlueMap successfully in our own discovery projects and are eager to apply it to even more. https://lnkd.in/egYv7vkE I want to highlight the power of combining physics-based simulations with machine learning. In GlueMap, we trained a variational autoencoder on extensive molecular dynamics simulations to capture a latent structural representation of ternary complexes. This helped us build a model that predicts degradation potency using training data from wet-lab measurements of just five molecular glues. Our results hint that, by pairing simulation data—cheap and quick to generate—with limited experimental data—costly and slow, physics-informed few-shot machine learning may create powerful predictive models. Team work by Jesus Izaguirre, Yujie Wu, Zach McDargh, PhD, Timothy Palpant, Asghar Razavi, PhD, Fabio Trovato, and Cheryl Koh from Atommap

Advancements in AI Technologies have enabled machines to learn from large datasets, improving speed of drug discovery, and disease diagnosis. One of the notable developments is the increasing use of Machine Learning (ML) and Deep Learning (DL) algorithms in healthcare. These technologies have enhanced drug discovery and disease diagnosis, reducing costs, and time. #FutureGRINNextGen #AI #Technology #ML #DL #Drugdisccovery #Diseases

AI has officially stepped into the lab, and it’s working wonders." AlphaFold, developed by DeepMind, has achieved what was once thought impossible—accurately predicting protein structures in record time. Why does this matter? 1?? It accelerates drug discovery and treatment development. 2?? It helps tackle previously unsolvable diseases. 3?? It unlocks a deeper understanding of human biology. With AlphaFold, the future of medicine and research has never looked brighter. How do you see this impacting global healthcare? #AlphaFold #HealthcareInnovation #AIInBiology

What’s the one thing we can’t afford to compromise on? Our health. Artificial Intelligence is now at the forefront of revolutionizing medicine streamlining diagnoses, enabling personalized treatments, and even predicting outbreaks before they happen. Here are some key trends reshaping the future of healthcare: 1?? AI-driven diagnostics: Faster and more accurate than ever. 2?? Personalized medicine: Treatments designed just for you. 3?? Robotic surgeries: Precision beyond human limits. The question isn’t whether AI will change medicine—it’s how prepared we are to leverage these advancements. Are you ready? #AIInMedicine #HealthcareInnovation

True innovation doesn’t just follow trends—it sets the path for others to follow. A thought leader doesn’t wait for change; they create it, shaping the future with every idea. Embrace the journey ahead and keep pushing the boundaries.