SmartGateVC的动态

?? AI in Drug Discovery: A Game Changer for the Next 5 Years The global AI in Drug Discovery market is expected to see impressive growth, with a projected rate of 25-30% from 2024 to 2029. ?? Key drivers behind this surge include: ? Growing adoption of AI in healthcare & life sciences? ? Large-scale data generation in life sciences? ? Enhanced computing power and generative AI for drug design? ? Pharma-AI collaborations fueling innovation? ? Increasing demand for personalized medicine However, challenges such as high implementation costs, data privacy, and regulatory concerns remain. ?? The market includes key players like Atomwise., BenchSci, BenevolentAI, BPGbio, Inc., Deep Genomics, Exscientia, Google, iktos, Illumina, IBM, Insilico Medicine, insitro, Microsoft, NVIDIA, NuMedii, Recursion, Schr?dinger, Tempus AI, Valo Health, Verge Genomics, and XtalPi Inc., and more. Download the report to dive deeper into this evolving landscape: https://lnkd.in/g7SC5ewC Follow on Medi-Tech Insights #AI #DrugDiscovery #HealthcareInnovation #PharmaTech #PersonalizedMedicine #LifeSciences #AIHealthcare #AIinPharma #MedicalTechnology #MedTech #Healthcare #AIDrugDiscovery #DigitalHealth #HealthcareAI #BiopharmaInnovation #PrecisionMedicine #GenerativeAI #PharmaAI #MachineLearningInHealthcare #HealthTech #AIInBiotech #DeepLearningInPharma #AIForDrugDevelopment #BigDataInHealthcare #ArtificialIntelligenceInMedicine #HealthcareDataAnalytics #PharmaCollaborations #InnovationInHealthcare #DrugDevelopmentEfficiency #SmartHealthcare #AIRegulationsInHealthcare #Google, #ChatGPT #Bing #Gemini #ChatBot

Janus Flow: An Alternative to Stable Diffusion and it’s significance in Drug (Drug Discovery & Development ) Discovery: What is DeepSeek AI Janus Flow? Janus Flow ? Seamless Integration: Combines image understanding and generation in a unified model, eliminating the need for separate systems. ? Computational Efficiency(Machine Learning) Operates as a single system for training without compromising quality in image understanding or generation. ? Advanced Generation Approach: Utilizes Rectified Flow for faster, higher-quality image generation, leveraging ODEs instead of iterative diffusion. This makes it especially well-suited for integration with large language models (LLMs). Why It Matters for Drug Discovery Janus Flow’s ability to merge image analysis (e.g., microscopy data, protein structures) with text-based insights (e.g., research articles, and clinical data) can: ? Accelerate target identification and drug design. ? Improve predictions of drug interactions and side effects. ? Significantly reduce research timelines. These capabilities are critical for delivering life-saving therapies to the market more efficiently. Bright future ahead for Drug discovery and trials industry.?? #deepseekR1 #deepseek #stablediffusion #nvidia #bigtech #magnificentseven #artificialintelligence #machinelearning #openai #chatgpt

Thrilled to share my recent experience at the Workshop on Artificial Intelligence for Life Sciences, Healthcare, and Biotechnology by Amity Centre for Artificial Intelligence (ACAI). A deep dive into ML and DL models has broadened my understanding, thanks to the incredible instructors. I'd also like to extend my gratitude towards my guide Dr. Puniti Mathur for encouraging me to join the workshop. #HealthcareInnovation #Biotech #MachineLearning #DeepLearning

A Complete Guide On #AI_in_Clinical_Trials AI in clinical trials enhances efficiency and accuracy by streamlining processes such as patient recruitment, data analysis, and trial management. It leverages algorithms to identify suitable participants, improving diversity and adherence to protocols. AI also analyzes vast datasets to detect patterns and predict outcomes, aiding in real-time decision-making. By automating repetitive tasks, it reduces human error and accelerates timelines. Furthermore, AI-powered tools facilitate personalized medicine by analyzing genetic information, allowing for tailored treatment approaches. ??For More Information -: https://bit.ly/4evb6H2 KeyPlayers - IBM, Exscientia, Saama Technologies, Unlearn.AI, BioSymetrics, Euretos, Insilico Medicine, Ardigen, PHARMASEAL, Intel Corporation, Numerate, AiCure, Envisagenics, Nuritas, BioAge Labs, SymphonyAI., Median Technologies, Innoplexus - a Partex Company, Antidote Technologies, GNS, Halo Health, and Deep Lens (acquired by Paradigm) #aiinclinicaltrials #clinicalai #aiinhealthcare #digitalhealth #aidrugdiscovery #precisionmedicine #clinicalresearch #aiinmedicine #smartclinicaltrials

?? AI in Drug Discovery: A Game Changer for the Next 5 Years The global AI in Drug Discovery market is expected to see impressive growth, with a projected rate of 25-30% from 2024 to 2029. ?? Key drivers behind this surge include: ? Growing adoption of AI in healthcare & life sciences? ? Large-scale data generation in life sciences? ? Enhanced computing power and generative AI for drug design? ? Pharma-AI collaborations fueling innovation? ? Increasing demand for personalized medicine However, challenges such as high implementation costs, data privacy, and regulatory concerns remain. ?? The market includes key players like Atomwise., BenchSci, BenevolentAI, BPGbio, Inc., Deep Genomics, Exscientia, Google, iktos, Illumina, IBM, Insilico Medicine, insitro, Microsoft, NVIDIA, NuMedii, Recursion, Schr?dinger, Tempus AI, Valo Health, Verge Genomics, and XtalPi Inc., and more. Download the report to dive deeper into this evolving landscape: https://lnkd.in/g7SC5ewC Follow on Medi-Tech Insights #AI #DrugDiscovery #HealthcareInnovation #PharmaTech #PersonalizedMedicine #LifeSciences #AIHealthcare #AIinPharma #MedicalTechnology #MedTech #Healthcare #AIDrugDiscovery #DigitalHealth #HealthcareAI #BiopharmaInnovation #PrecisionMedicine #GenerativeAI #PharmaAI #MachineLearningInHealthcare #HealthTech #AIInBiotech #DeepLearningInPharma #AIForDrugDevelopment #BigDataInHealthcare #ArtificialIntelligenceInMedicine #HealthcareDataAnalytics #PharmaCollaborations #InnovationInHealthcare #DrugDevelopmentEfficiency #SmartHealthcare #AIRegulationsInHealthcare #Google, #ChatGPT #Bing #Gemini #ChatBot

?? Revolutionizing Drug Discovery with AI In an age where time and costs are pivotal in drug development, the integration of artificial intelligence (AI) is poised to transform traditional practices. Conventional drug discovery can stretch over 15 years and cost upwards of $2 billion. However, AI enables researchers to expedite drug identification and streamline the overall discovery process significantly. ?? Innovative Solutions Innoplexus, a registered NVIDIA Inception startup, showcases how AI technologies such as deep learning, AlphaFold2 for protein structure prediction, and DiffDock for molecular docking are shaping the future of drug discovery. By leveraging advanced computational tools, they are enhancing efficiencies specifically in targeting neurodegenerative diseases linked to TDP-43 aggregation. ?? High-Throughput Screening With the ability to screen approximately 5.8 million compounds in just a few hours and achieve 90% accuracy in optimizing lead compounds, the capabilities of AI in the drug discovery pipeline are unparalleled. This leap forward further enhances the potential for more effective treatments while drastically reducing development times and costs. ?? Future Implications As AI continues to redefine the landscape of drug discovery, professionals in software development, marketing, and biotechnology must embrace these innovations to stay competitive. The advancements made by initiatives like Innoplexus herald a new era in the battle against diseases, fulfilling urgent medical needs. Stay Ahead in Tech! Connect for cutting-edge insights and knowledge sharing! Want to make your URL shorter and more trackable? Try linksgpt.com #AI #DrugDiscovery #BitIgniter #LinksGPT Want to know more: https://lnkd.in/eHtUv78f

?? AI in Drug Discovery: Are We Moving Too Fast? ?? The use of AI in drug discovery has unlocked exciting new frontiers. From screening millions of compounds to predicting molecular interactions, the speed and efficiency are undeniable. But as scientists, we need to pause and ask ourselves: Can we fully trust the results? AI-driven models rely heavily on data, but biological systems are incredibly complex, and data gaps still exist. Are the algorithms accounting for variables like genetic variability, unexpected side effects, or environmental influences? In a field where precision is paramount, small miscalculations could have enormous consequences. Moreover, how much do we understand the decision-making process of these models? The "black box" nature of AI can sometimes leave us with more questions than answers. As we adopt these technologies, how do we ensure human oversight remains at the core? What are your experiences working with AI in the lab? How can we strike the right balance between innovation and caution in drug discovery? Let’s dig deeper into this fascinating intersection of technology and biology! ?? #DrugDiscovery #AIinScience #ScientificIntegrity #PharmaTech #Innovation #Biotech #AI

Will AI revolutionize drug development? Researchers explain why it depends on how it's used: The potential of using artificial intelligence in drug discovery and development has sparked both excitement and skepticism among scientists,?... https://dlvr.it/THB49f

Biotech meets AI: Faster drug discovery ?? In 2024, the biotech industry is leveraging AI to turbocharge drug discovery. Here’s how: - ?? AI models analyse massive datasets, predicting promising compounds faster than any human. - ?? Machine learning identifies potential drug candidates, reducing the time and cost of R&D. - ?? Predictive analytics streamline clinical trials by identifying suitable participants and improving trial designs. These advancements mean quicker development of life-saving drugs, transforming healthcare for the better. What’s your take on AI in biotech? Share your thoughts below! #Biotech #AI #DrugDiscovery

How AI & Quantum Computing Are Tackling the Biggest Flaw in Drug Discovery: Tailoring Treatments That Work for Everyone We all know how slow and expensive the drug discovery process is. On average, it takes 8-10 years and billions of dollars to bring a new drug to market—only for 90% of those drugs to fail in trials. Even the ones that succeed may not work for everyone. But what if we could change that? ?? ?? Quantum computing and AI are making waves in the biopharma world. These tools can simulate molecular interactions at a quantum level, allowing us to discover and refine drugs much faster and more efficiently. The potential? Shorter development times, lower costs, and more personalized treatments. ?? Sandbox AQ is already collaborating with leading institutions like UCSF to apply this technology, showing promising results in cutting down the drug development timeline. This raises some interesting questions: Could this be the breakthrough we need to revolutionize healthcare? What other industries could benefit from a similar quantum-AI approach? How do we ensure that these advancements are accessible to everyone, not just large pharmaceutical companies? Let’s discuss!?? How do you see AI and quantum computing reshaping drug discovery and beyond? What other areas could benefit from this game-changing technology? #QuantumComputing #AI #PharmaInnovation #DrugDiscovery #HealthcareRevolution #FutureOfMedicine https://lnkd.in/ge8D3x25