[{"data":1,"prerenderedAt":413},["ShallowReactive",2],{"blog-post-mcp-servers-for-cheminformatics-and-drug-discovery":3,"blog-posts-nav":22},{"data":4,"meta":18},[5],{"id":6,"attributes":7},87,{"title":8,"content":9,"createdAt":10,"updatedAt":11,"publishedAt":12,"date":13,"description":14,"keywords":15,"slug":16,"category":17},"MCP servers for cheminformatics and drug discovery","Nanome offers MCP servers and a Claude Code Skill that let an AI model drive real cheminformatics and drug discovery tools: docking, structure prediction, and analysis. MCP (Model Context Protocol) is the open standard that connects a language model to outside tools and data, so instead of an LLM guessing at chemistry, it calls the actual programs through Nanome's AI copilot, MARA, which reaches [more than 300 integrated scientific tools](https:\u002F\u002Fnanome.ai\u002Fintegrations). The servers [install inside your own network](https:\u002F\u002Fnanome.ai\u002Fmara-enterprise), as a single tenant in the cloud or on hardware your group owns, and they take an internal or custom LLM as the model behind them.\n\n## What an MCP server does for chemistry\n\n![A plain-language request passes through an MCP gateway and triggers three distinct scientific tools on the other side.](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fmcp_servers_for_cheminformatics_and_drug_discovery_image_1_db521186a2.png)\n\n\n\nMCP (Model Context Protocol) is a shared way for an AI model to talk to external tools. You stand up an MCP server that lists a set of functions, and any MCP-aware client (Claude Code, other agents, your own app) can call them.\n\nA language model has no docking engine inside it. It can produce a fluent paragraph about a binding pose having run no calculation at all. An MCP server puts a working engine on the other end of the call: the model chooses the function and reads what comes back, and the chemistry belongs to the program that ran it.\n\nThat is what Nanome ships. The MCP server hands MARA's tools to whatever model you point at it, so an AI can drive a docking run, kick off a structure prediction, or fetch a compound and open it up, all from plain-English requests.\n\n## What the Nanome MCP tools expose\n\nThrough MARA, the server puts these categories within reach of a connected model:\n\n- **Docking.** [Pose prediction and scoring for a ligand against a target pocket](https:\u002F\u002Fdocs.nanome.ai\u002Fmara\u002Ffeatures), with Smina and DiffDock-L behind it.\n- **Structure prediction and co-folding.** [Boltz-2 and AlphaFold 3 fold a sequence](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fsetting-up-boltz-2-configuration-files-and-analysis-with-nanome-ai), and either one will fold a protein together with its ligand to predict the bound complex.\n- **Electrostatics.** APBS solves the surface charge on a protein, with PDB2PQR ahead of it to sort out protonation.\n- **ADMET prediction.** Absorption, distribution, metabolism, excretion, and toxicity estimates on a candidate compound.\n- **Antibody annotation and binder design.** [ANARCI numbers variable domains and marks the CDR loops, ProteinMPNN writes sequences onto a backbone, and RFdiffusion3 (beta) generates de novo binders](https:\u002F\u002Fnanome.ai\u002Fagents).\n- **Cheminformatics.** Descriptors, similarity, filters, R-group work, and matched molecular pairs.\n- **Data pulls.** A model can fetch a structure from RCSB PDB, a compound from PubChem, or a record from DrugBank, and it opens in the session ready to inspect.\n\nEvery call leaves a trace. MARA names the tool it fired, the arguments it passed, and the artifact that came back, so a result delivered over MCP can be checked the same way as one produced at a terminal. The same library seen from inside the product, rather than through the protocol, gets walked through in [an AI copilot for drug discovery workflows](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fan-ai-copilot-for-drug-discovery-workflows).\n\nGroups add tools of their own. An in-house scoring script, or a model a team trained on its own series, gets wrapped as a MARA tool with a name and a signature, and the server then lists it beside the standard ones for any connected model to call.\n\n## MCP server or Claude Code Skill\n\nNanome ships 2 ways to connect a model, and they answer slightly different questions.\n\n\u003Ctable class=\"table\">\n  \u003Cthead>\n    \u003Ctr>\u003Cth>Option\u003C\u002Fth>\u003Cth>What it is\u003C\u002Fth>\u003Cth>Best fit\u003C\u002Fth>\u003C\u002Ftr>\n  \u003C\u002Fthead>\n  \u003Ctbody>\n    \u003Ctr>\u003Ctd>Nanome MCP server\u003C\u002Ftd>\u003Ctd>An MCP endpoint that lists MARA's tools to any MCP-aware client\u003C\u002Ftd>\u003Ctd>A model, an agent, or your own application has to call docking, folding, and analysis programmatically\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>Nanome Claude Code Skill\u003C\u002Ftd>\u003Ctd>A packaged Skill that teaches Claude Code how to drive Nanome and MARA\u003C\u002Ftd>\u003Ctd>Claude Code is already the working environment, and chemistry steps belong inside a coding or analysis session\u003C\u002Ftd>\u003C\u002Ftr>\n  \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\nThe MCP server is the general connector, usable from anything that speaks the protocol. The Skill is the shorter path when Claude Code is where the work already happens. Chaining several calls into one longer autonomous run carries its own tradeoffs, covered in [agentic AI for computational chemistry](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fagentic-ai-for-computational-chemistry).\n\n## Your network, your model\n\n2 constraints tend to govern this work once the compounds are proprietary and the target is unpublished.\n\nDeployment is the first. Nanome, MARA, and the MCP servers install inside your own network, as a single tenant in the cloud or on hardware your group owns. A docking run never has to cross the boundary to finish.\n\nThe reasoning layer is the second. MCP is model-agnostic, so the same Nanome tools answer to a hosted commercial model or to a fine-tuned one on your own GPUs. The server takes whichever model a security review has already cleared.\n\nTogether those two keep an AI-driven run inside the same perimeter the rest of the program already operates in.\n\n## When a direct tool call is simpler\n\n\u003Cdiv class=\"logo-row\" style=\"display:flex;flex-wrap:wrap;align-items:center;justify-content:center;gap:1.75rem;background:#fff;border-radius:12px;padding:1.25rem 1.5rem;margin:2rem 0\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fmcp_servers_for_cheminformatics_and_drug_discovery_image_5_baebdd1ecd.png\" alt='CDD Vault' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fmcp_servers_for_cheminformatics_and_drug_discovery_image_6_4d4c1ec5ed.png\" alt='KNIME' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fmcp_servers_for_cheminformatics_and_drug_discovery_image_7_031da381cb.png\" alt='Jupyter' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fmcp_servers_for_cheminformatics_and_drug_discovery_image_8_463faa8451.png\" alt='GROMACS' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003C\u002Fdiv>\n\n\n\nA fixed pipeline that fires the same docking job every night is served fine by a scripted call to the program underneath. Routing it through a model adds a decision step the job never needed. MCP pays off on exploratory work, where what runs next depends on what the last run returned.\n\nNanome sits next to the software a group already owns. It connects to [Schrödinger LiveDesign](https:\u002F\u002Fnanome.ai\u002Fintegrations), [CDD Vault](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fcollaborative-drug-discovery-and-nanome-partnership-announcement), [KNIME](https:\u002F\u002Fwww.knime.com), and [Jupyter](https:\u002F\u002Fjupyter.org), so the common arrangement keeps all of that and adds the MCP server for the work where a model picks the next step.\n\nOn the Schrödinger side, `.mae` and `.maegz` files open in Nanome directly, and that is the same format LiveDesign hands structures over in, so prepared work arrives without a rebuild. [More on supported formats](https:\u002F\u002Fdocs.nanome.ai\u002Fnanome_web\u002Ffileformats).\n\n## Proven on real molecules\n\n![Two colleagues review a ribbon-rendered protein structure together on a large display in a modern lounge setting.](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fmcp_servers_for_cheminformatics_and_drug_discovery_image_3_b584d7a212.png)\n\n\n\nInsilico Medicine generated 10 novel SARS-CoV-2 protease inhibitors with AI, then brought them into Nanome for the medicinal chemistry review before anything went to synthesis. The work was co-authored and posted to ChemRxiv. Alex Zhavoronkov, the company's CEO, gave the reason for that step: \"it is important for medicinal chemists to look at these molecules closely before placing a billion-dollar, life-or-death wager.\"\n\nThat review is the argument for wiring a model to real tools. A generated molecule still has to be judged by a chemist, and a call that returns a real structure puts the thing itself in front of them. What other groups have done with the platform is collected in the [case studies](https:\u002F\u002Fnanome.ai\u002Fcase-studies).\n\n## FAQ\n\n**Is there an MCP server for cheminformatics?**\nYes. Nanome runs one, and it exposes cheminformatics and drug discovery tools through MARA. A connected model can calculate descriptors, run similarity searches and filters, dock a ligand, predict a structure, and work through the rest of a library spanning more than 300 integrated tools across 26 categories.\n\n**How do I connect an LLM to chemistry tools?**\nAny MCP-aware client can point at the Nanome MCP server, and teams already working in Claude Code can install the Nanome Claude Code Skill instead. Either way the model calls docking, folding, electrostatics, and ADMET as functions, and the numbers come back from the programs themselves.\n\n**Can the MCP server run on private data?**\nYes. Nanome, MARA, and the MCP servers install inside your own network, as a single tenant in the cloud or on hardware you own, and an internal or custom LLM can drive them, so proprietary structures stay where they started.\n\n**What can an AI actually do through the Nanome MCP server?**\nDock a ligand (Smina, DiffDock-L), predict a structure or co-fold a complex ([Boltz-2](https:\u002F\u002Fgithub.com\u002Fjwohlwend\u002Fboltz), [AlphaFold 3](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-024-07487-w)), solve electrostatics with APBS, estimate ADMET and toxicity, number antibody variable domains with ANARCI, design sequences onto a backbone with ProteinMPNN, generate binders with RFdiffusion3, and run cheminformatics calculations, each with a record of the call and what it returned.\n","2026-07-15T01:23:50.592Z","2026-09-03T16:00:28.589Z","2026-09-03T16:00:28.330Z","2026-09-03","MCP server for cheminformatics and drug discovery: Nanome exposes docking, structure prediction, and analysis to an LLM behind your firewall.","MCP server cheminformatics, MCP server drug discovery, connect an LLM to chemistry tools, Model Context Protocol, Claude Code Skill, Nanome, MARA, docking, structure prediction","mcp-servers-for-cheminformatics-and-drug-discovery","frequent-topics",{"pagination":19},{"page":20,"pageSize":21,"pageCount":20,"total":20},1,100,{"data":23,"meta":411},[24,29,35,40,45,50,55,60,65,70,75,81,86,92,97,102,107,112,117,122,127,132,137,142,147,153,158,163,168,173,178,183,188,193,198,203,208,213,218,223,228,233,238,243,248,253,258,263,268,273,278,283,288,293,298,303,309,314,319,324,329,334,339,344,349,354,359,364,369,374,379,384,389,394,399,401,406],{"id":20,"attributes":25},{"slug":26,"title":27,"category":28},"spy-stories-and-rational-drug-design","Spy stories & rational drug design","case-studies",{"id":30,"attributes":31},2,{"slug":32,"title":33,"category":34},"meta-quest-pro-and-a-new-version-of-nanome-(v1.24)","Meta Quest Pro & a new version of Nanome (v1.24)","releases",{"id":36,"attributes":37},3,{"slug":38,"title":39,"category":28},"beating-the-pandemic-in-virtual-reality","Beating the Pandemic in Virtual Reality",{"id":41,"attributes":42},4,{"slug":43,"title":44,"category":28},"a-new-era-in-drug-discovery-the-first-ai-generated-drug-is-going-to-clinical-trial","A new era in drug discovery? 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