Rammohan Pratapa’s Post

🔬 Unlocking the Power of LLMs in Biopharma with Aganitha’s ARC™ Framework Large Language Models (LLMs) like ChatGPT will transform biomedical research, clinical trials, and medical affairs, but their integration requires strategic solutions. 🔍 Challenges in implementing LLMs for Biopharma 1. Common Pitfalls: Off-the-shelf models may produce hallucinations, struggle with math & stats, and fail to meet stringent Protected Health Information (PHI) requirements. 2. Private data integration: Cannot leverage valuable private data out of the box  3. Operational Hurdles: Training costs and deployment complexities pose significant barriers. 🚀 Aganitha’s Solution: The ARC™ Framework ARC™ combines LLM capabilities with biopharma expertise, offering customized tools and services. - Omics Data Insights: Utilize AI-powered tools to analyze genomic, transcriptomic, and proteomic data. - Hypothesis Generation: Analyze diverse datasets to generate testable hypotheses and discover novel drug targets and biomarkers. - Document workflows: Generate, update, compare and review documents such as regulatory submissions and contracts. - Reduced Hallucination: Minimize inaccurate outputs by incorporating authoritative datasets and ontologies. 👉 📽️ Watch ARC™ demos at https://lnkd.in/gWw5QKQj #Biopharma #AI #LLM #ChatGPT

It's incredible to see how #ai is transforming efforts of University of Illinois Urbana-Champaign alumni in the field of biopharma, drug discovery, clinical trials and more. Prasad Chodavarapu is one such alum innovating in this realm. This underscores why U of I created the world's first engineering-focused college of medicine, Carle Illinois College of Medicine.

Happy to introduce ARC (AI as research collaborator) to do your analysis, to run your reports, and provide you biology specific insights, to propel the pace of your drug discovery research. Checkout the videos to understand the capabilities and reach out to see what ARC can do

Congratulations to the Aganitha team for this great leap in AI-based solutions! The ARC™ Framework is set to revolutionize biomedical research, clinical trials, and medical affairs by integrating advanced Large Language Models (LLMs) like ChatGPT with biopharma expertise. While off-the-shelf models like ChatGPT are powerful, they often face challenges such as producing hallucinations, struggling with math & statistics, and failing to meet stringent Protected Health Information (PHI) requirements. Aganitha’s ARC™ Framework addresses these issues, offering a tailored solution for the biopharma industry. For those in biopharma looking to overcome these hurdles and enhance their workflows, this could be the game-changer you've been waiting for. Explore how ARC™ can address common challenges, integrate valuable private data, and streamline your operations.

Interested in how Large Language Models can be applied in biopharma? Check out this insightful playlist launched by Aganitha! This series explores practical applications of LLMs across various areas, including omics research, clinical trials, and medical affairs. I encourage all biopharma professionals and researchers to explore these demos and see how LLMs can enhance their work. #LLMs #ai #biopharma

🚀 Discovering the Potential of Large Language Models in Drug Development 🧬✨ I recently came across a fascinating paper titled "Comparative Analysis of LLaMA and ChatGPT Embeddings for Molecule Embedding" from the University of Windsor, Canada. This research explores how Large Language Models (LLMs) like LLaMA and ChatGPT can be applied to cheminformatics, particularly focusing on molecular property prediction and drug-drug interaction prediction. 🔍 Key Highlights: 🏆 Performance: The study found that LLaMA-based SMILES embeddings outperform those generated by ChatGPT in predicting molecular properties and drug-drug interactions. 🎯 Applications: These LLMs have shown significant promise in advancing drug development by providing novel approaches to understanding chemical graphs. 💡 Conclusion: The research underscores the potential of LLMs in cheminformatics, highlighting their role in improving drug discovery processes. If you're interested in how AI and machine learning are transforming healthcare and drug development, this paper is definitely worth a read! 🔗Check out the full paper here: https://lnkd.in/euw5QgQf #ArtificialIntelligence #MachineLearning #DrugDevelopment #HealthcareInnovation #MolecularEmbedding #Cheminformatics #Research

AI has helped life sciences speed up the drug discovery process for years...way before ChatGPT was a shimmer in a developer's eye. AI and machine learning are saving lives with protein drug development and better antibody creation. But the document authoring and submission process that supports bringing new drugs to market is not maintaining the same pace. Join Regina Lynn Preciado and David M. Turner as they discuss how AI technology combined with structured content authoring can make innovation soar and produce outcomes that support patients as well as your bottom line. https://lnkd.in/eFiztUyN #structuredcontentauthoring #AIanddrugdevelopment #DCLLearningSeries

Everyone agrees there's high anticipation around AI's impact in drug development. However, in order for AI to reach its full impact in drug development, it's not as easy as tossing searches into ChatGPT. The successful application of AI to drug development requires expertise in knowing how best to incorporate AI into research as well as technology that appropriately integrates AI with other in silico approaches, such as QSP and PBPK. This article by Amin Rostami-Hodjegan and Piet Van Der Graaf demonstrates the various applications of AI to drug development currently implemented at Certara. View the full article at: https://ow.ly/Z8TM50SAqK8 #AI #DrugDiscovery #DrugDevelopment #QuantitativeSystemsPharmacology #PBPK

ChatGPT: A double-edged sword for scientific writing The use of artificial intelligence (AI) in academic paper writing is witnessing a surge in popularity. Specifically, ChatGPT, a member of the generative pre-trained transformer (GPT) family of large language models (LLMs), has found application across diverse scientific and medical domains, encompassing detection of drug interactions, drug safety monitoring, medical image analysis, and the refinement of research papers. Read more via #cell #theinnovation https://lnkd.in/du-f7TbE #ChatGPT #ScientificWriting #AIinAcademia #GPTFamily #ArtificialIntelligence #AcademicResearch #MedicalAI #DrugInteractions #DrugSafety #MedicalImageAnalysis #ResearchPapers #LanguageModels #AIApplications #ScienceandTechnology #InnovationinWriting #AcademicInnovation #AIforResearch #TechinMedicine #ScientificAdvancements #AIinMedicine #MedicalResearch #DataAnalysis #InnovativeTech #AIProgress #ResearchRevolution

With the emergence of ChatGPT the application of generative AI across various industries has sparked widespread discussion. In the field of new drug development, from traditional AI to generative AI, can artificial intelligence technology completely revolutionize the conventional processes? What are the challenges in its application? Where does China stand in this race? Addressing these questions, recently, SFC reporter interviewed Rumin Zhang, PhD, Chief Scientist of the Global Health Drug Discovery Institute (GHDDI), at the Zhongguancun Forum. He mentioned that traditional AI has already made incremental contributions to new drug development, hoping that generative AI can become a comprehensive solution, evolving from a laboratory assistant to a team leader and truly becoming a "weapon" for new drug development. Zhang pointed out that considering the language comprehension abilities demonstrated by generative AI, it has the potential to think like a group of highly skilled pharmaceutical scientists. He said, If AI can embody the collective wisdom and knowledge of thousands of pharmaceutical scientists and empower new drug development, it would be remarkable because it possesses all the knowledge and experience of our pharmaceutical industry, while also being capable of innovative thinking based on comprehension. This is the revolutionary impact that generative AI can bring to new drug development. Full Interview: https://lnkd.in/gpKeVxKs