AI-Enabled Scientific Collaboration Tools

New paper out in Nature Computational Science! Introducing #SciSciGPT: an open-source, multi-agent, prototype AI collaborator designed to support research and discovery, using the science of science as a testbed. Led by the amazing Erzhuo Shao. SciSciGPT explores a simple idea: What if we enable LLMs to understand the domain-specific literature, the data available to use for research, and the tools for analysis and visualization? Through an interactive chat interface, SciSciGPT builds on frontier models and orchestrates auditable, end-to-end workflows for: - literature understanding - data extraction - analysis & visualization - self-evaluation Importantly: SciSciGPT is a prototype. Its value lies in the integration of existing AI capabilities into a transparent, domain-grounded research workflow that supports scientific inquiry. If designed appropriately, such a system could substantially increase research efficiency, lower barriers to entering the field, facilitate reproducibility, and support early-stage exploration and idea generation. In case studies + an exploratory user study, tasks took minutes instead of hours, and outputs received higher expert ratings. We compare it with human researchers across career stages, who use general AI tools to complete the same tasks. SciSciGPT points to the broader idea of designing domain-grounded AI collaborators, which can be tailored to many other data-intensive fields, supporting discovery, exploration, and reproducibility at scale. This opens up many exciting new possibilities. At the same time, they also raise big questions about transparency, ethical use, authorship, and the ways we train the next generation of scientists. We are entering a golden era of research and discovery, powered by AI agents. The potential is immense. And so are the risks. Must be navigated with care, transparency, and thoughtful design. SciSciGPT is our first step. Many more to come. Stay tuned! The full paper: https://lnkd.in/gvsqprqU All codes are open-source: https://lnkd.in/gNiSUS8J The research briefing: https://lnkd.in/gmdR2yik And experiment with SciSciGPT yourself at https://sciscigpt.com!

🧬 Turn Claude into Your Personal AI Scientist Imagine having an AI research assistant that can: → Query 24 major scientific databases (PubMed, ChEMBL, AlphaFold, UniProt...) → Work with 40+ specialized packages (RDKit, BioPython, DeepChem, PyTorch Geometric...) → Connect to your lab infrastructure (Benchling, Opentrons, DNAnexus, OMERO...) → Apply 122 documented workflows across bioinformatics, cheminformatics, ML, and materials science This is the equivalent of thousands of 'tools'! That’s exactly what Claude Scientific Skills delivers. This open-source collection from the K-Dense team transforms Claude Code into a powerful scientific computing environment. Whether you’re analyzing genomic data, designing molecules, processing mass spectrometry results, or running multi-omics analyses. Getting started is ridiculously simple: /plugin marketplace add K-Dense-AI/claude-scientific-skills Then browse and install the packages you need. No complex setup. No endless configuration files. Just instant access to cutting-edge scientific tools. The best part? It includes "Scientific Thinking" skills, from exploratory data analysis and hypothesis generation to peer review and scientific writing. It’s not just about running code; it’s about doing better science. 🔬 Bioinformatics researchers: Analyze RNA-seq, run GWAS, query protein structures 💊 Drug discovery teams: Screen compounds, predict ADMET, perform molecular docking 🧪 Lab scientists: Automate protocols, manage ELN data, process experimental results 📊 Data analysts: Run statistical tests, create publication-quality figures, integrate multi-omics data 👉 Check it out: https://lnkd.in/eE9rzy8K What scientific workflow would you automate first? Drop your thoughts in the comments! 👇 #ScientificComputing #AI #Bioinformatics #DrugDiscovery #MachineLearning #OpenSource #ResearchTools #LabAutomation

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.

After testing 50+ AI tools, these 8 free options maintain complete academic integrity. Most academics avoid AI completely. They're terrified. But here's what they're missing: Not all AI tools violate integrity. Some actually enhance it. The difference is knowing which ones. Picture this researcher nightmare: You use ChatGPT for literature review. Submit your paper. Editor runs plagiarism detection. Flags AI-generated content. Immediate rejection. Your reputation damaged permanently. After testing every major AI research tool, I found the truth. Eight tools actually improve academic integrity. They help you find better sources. Analyze research more thoroughly. Never generate content for you. The 8 integrity-safe AI research tools: 1. Semantic Scholar - Discovers relevant research papers using AI search - Helps find sources you'd never locate manually - Shows citation context and paper influence 2. Elicit - Assists systematic literature reviews - Extracts key findings from multiple papers - Organizes research themes automatically 3. Research Rabbit - Maps citation networks visually - Reveals research connections and trends - Helps identify influential papers quickly 4. Connected Papers - Creates visual literature landscapes - Shows relationships between studies - Guides research direction discovery 5. Scite - Analyzes how papers cite each other - Distinguishes supporting vs contradicting citations - Improves research quality assessment 6. Litmaps - Visualizes research evolution over time - Tracks how ideas develop chronologically - Identifies research gaps and opportunities 7. Inciteful - Recommends papers based on your interests - Uses AI to suggest relevant literature - Personalizes research discovery process 8. Consensus - Synthesizes evidence across studies - Provides AI-powered research summaries - Helps evaluate scientific consensus The secret successful researchers know: AI can be your research accelerator. Not your content creator. Use it to find and analyze. Never to write or generate. These tools enhance human intelligence. They don't replace it. Help you work smarter. Never compromise your ethics. Your research deserves the best tools available. As long as they maintain your integrity. Which AI research tool will you try first? Save this post. Your research efficiency depends on it. Follow me for more ethical AI strategies that enhance academic work.

🤖 Building AI-Friendly Scientific Software: A Model Context Protocol (MCP) Journey I just released a new blog post detailing how I built a remote MCP server to giveAI agents expert-level knowledge of my scientific codebase, Napistu — and the results are a game-changer. The Problem: AI can accelerate development, but without deep project context, agents often reinvent rather than reuse code, miss the intent behind the implementation, and generate brittle or bloated code. The Solution: The Napistu MCP server gives AI real-time access to structured, evolving project knowledge by:  ✅ Unifying scattered resources — docs, tutorials, GitHub issues/PRs — into consistent, queryable endpoints ✅ Enabling natural langue semantic search across technical content ✅ Running in the cloud with automatic updates tied to codebase changes (all for <$1/day) The Results: A/B testing shows dramatic improvement. Instead of manually curating context files, agents now provide holistic guidance that ties together theory, tutorials, and current development — just like a real domain expert. Ready to try it? If you're working on or curious about biological networks — as a user or developer — the server is live and requires no local installation. Just configure Claude (or your preferred LLM) with the MCP server and explore Napistu through a personalized, AI-guided lens. I’d love your feedback — on the post, the server, or Napistu in general. Drop a comment or reach out! Whether you're learning systems biology, contributing to open source, or building your own scientific tools, this approach transforms how AI agents interact with complex domains. See the comments for a link to the full post #MCP #ScientificProgramming #AI #SystemsBiology #Claude #Napistu

Has AI changed the way you do research? Meet Sujay Kumar Jauhar, who is exploring how to build AI-native systems for long-term, collaborative workflows. One domain where this really matters is science, which is inherently collaborative and nonlinear, which makes it fun to reflect on how Sujay’s research provides insight into the work that we do as researchers. Sujay is essentially developing AI that can reason, plan, and collaborate like a scientist. His work on ResearchAgent (https://lnkd.in/giBds6Sy) introduced a team of agents that propose research questions, suggest methods, and even simulate peer review to help researchers come up with more creative ideas and refine their experiments. This was informed by his earlier work modeling how people break down complex tasks (https://lnkd.in/gedBNxxa) and manage time across competing priorities (https://lnkd.in/gNbnG8ki), both of which are essential for making progress in scientific inquiry. But, as I’ve discussed previously (https://lnkd.in/ga4BYuWR), the scientific process isn’t just about running experiments. Before forming a hypothesis or designing a study, researchers must understand the prior literature, and Sujay’s research has the potential to change the way we do that. He has developed methods to mine insights from massive text corpora (https://lnkd.in/gPksE28Z), which could help researchers keep up with the accelerating pace of publication, and explored ways to reduce AI errors using fine-grained user feedback (https://lnkd.in/gxGyb-ig) and automatic prompt rewriting (https://lnkd.in/gp4Bbzef), which is crucial in domains like science where getting things right is important. The way we disseminate research is likely going to evolve with AI, too, and Sujay’s research also provides insight into how people can communicate complex work like science more effectively. He’s studied how people collaborate on shared documents, showing how AI can support everything from initial brainstorming to final revisions (https://lnkd.in/gExjwCBk). He’s also worked on personalizing AI behaviors for different audiences (https://lnkd.in/gn-vvDUP), which could make scientific communication more adaptive and inclusive. If you’re not yet thinking about how AI will change your own research practices, you should be. And if you’re not yet following Sujay’s research, I highly recommend checking it out. #AIInnovators #AppliedResearch #HumanAI #OAR #LeadingLikeAScientist

🔬 Exciting Progress in AI for Science this week as Google Unveils AI Co-Scientist - A New Era of Accelerated Scientific Discovery! Key takeaways from this new paper published yesterday: 🤖 Introduction of AI Co-Scientist: Google has developed an AI system named "AI Co-Scientist," built on Gemini 2.0, designed to function as a virtual collaborator for scientists. This system aims to assist in generating novel hypotheses and accelerating scientific and biomedical discoveries. 👨👩👦👦 Multi-Agent Architecture: The AI Co-Scientist employs a multi-agent framework that mirrors the scientific method. It utilizes a "generate, debate, and evolve" approach, allowing for flexible scaling of computational resources and iterative improvement of hypothesis quality. 🧬 Biomedical Applications: In its initial applications, the AI Co-Scientist has demonstrated potential in several areas: 1. Drug Repurposing: Identified candidates for acute myeloid leukemia that exhibited tumor inhibition in vitro at clinically relevant concentrations. 2. Novel Target Discovery: Proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids. 3. Understanding Bacterial Evolution: Recapitulated unpublished experimental results by discovering a novel gene transfer mechanism in bacterial evolution through in silico methods. 🤝 Collaborative Enhancement: The system is designed to augment, not replace, human researchers. By handling extensive literature synthesis and proposing innovative research directions, it allows scientists to focus more on experimental validation and creative problem-solving. 💡 Implications for Future Research: The AI Co-Scientist represents a significant advancement in AI-assisted research, potentially accelerating the pace of scientific breakthroughs and fostering deeper interdisciplinary collaboration. This development underscores the transformative role AI can play in scientific inquiry, offering tools that enhance human ingenuity and expedite the journey from hypothesis to discovery.

OpenAI is positioning Prism as a “workspace for scientists.” That framing is far too modest. What’s actually happening is a quiet but decisive move up the value chain: from models → tools → ownership of the scientific workflow itself. Hypotheses, experiments, interpretation, iteration - all inside one AI-native environment. At that point, the model stops being infrastructure and becomes the operating system of discovery. That matters because whoever owns the workflow doesn’t just speed things up. They shape what gets explored, how uncertainty is handled, and which paths become economically viable. This isn’t neutral tooling. It’s epistemic leverage. What makes Prism more important than it looks is the precedent it sets. It normalizes the idea that serious thinking happens inside AI-native environments - where context is persistent, reasoning is collaborative (human + machine), and the interface is intent, not documents. Once that becomes normal in science, it won’t stay there. Strategy, engineering, finance, policy - everything that still assumes humans are the primary integrators is next. So the real question for research-heavy organizations isn’t “Should we adopt AI tools?” It’s which parts of our knowledge production we are willing to externalize - and under what governance. That’s not an IT decision. It’s a power decision. https://lnkd.in/edvU9sFY #AI #Transformation #Science #Future

One of the clearest signs that AI has crossed from potential to practice in science isn’t a benchmark score — it’s how many researchers are quietly using it every week🔬 Earlier this week, we shared data showing that 1.3 million people globally now use ChatGPT each week for advanced science and math work, sending 8.4 million messages across fields like biology, chemistry, physics, and engineering. That usage grew nearly 50% last year — not because it’s flashy, but because it fits into real research workflows. Today, OpenAI launched Prism, a new tool designed specifically for scientists and academics. Scientific research is getting harder. Economists have pointed to falling research productivity for years: more time, people, and money are required to generate the same insights. As knowledge gets more complex, researchers spend more time just reaching the frontier before they can even begin asking new questions. AI doesn’t replace scientists. It helps by reducing friction: • Synthesizing vast literatures • Translating ideas into code or math • Debugging analyses • Speeding up iteration from hypothesis to test That’s the role Prism is meant to play — meeting researchers where they already are, and supporting the work they’re already doing. Only about 0.1% of the global population are scientists, but their impact on health, economic growth, and resilience is outsized. Even small gains in how efficiently they work can compound quickly for society 📈 It’s still early — but it’s increasingly clear that AI is becoming a collaborator in scientific discovery, not just a tool on the side. #OpenAI #Prism #ChatGPT #Science