[{"data":1,"prerenderedAt":413},["ShallowReactive",2],{"blog-post-agentic-ai-for-computational-chemistry":3,"blog-posts-nav":22},{"data":4,"meta":18},[5],{"id":6,"attributes":7},74,{"title":8,"content":9,"createdAt":10,"updatedAt":11,"publishedAt":12,"date":13,"description":14,"keywords":15,"slug":16,"category":17},"Agentic AI for computational chemistry","Agentic AI for computational chemistry is an AI that plans a scientific task, picks the right tools, and runs them on real structures, then hands you the results. [Nanome's MARA](https:\u002F\u002Fnanome.ai\u002Fmara) is one of these. It's an AI copilot inside Nanome's molecular visualization and drug discovery platform, and it orchestrates [300+ integrated scientific tools](https:\u002F\u002Fnanome.ai\u002Fintegrations) from plain-English requests, across [the web app and XR headsets](https:\u002F\u002Fnanome.ai\u002Fsetup), behind your firewall.\n\nAsk a language model about docking and you get prose. Ask MARA, and a docking job runs against your protein and your ligand, then the pose comes back in 3D.\n\n## What \"agentic\" means here\n\n![A researcher wearing an ultra-thin VR headset studies a solid protein surface model with a small ligand visible in its binding pocket held close to the chest](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fagentic_ai_for_computational_chemistry_image_1_v4_d9a4b5a5c4.png)\n\n\n\nTwo separable jobs sit behind any useful answer. Something has to decide what to run, and something has to run it. A language model handles the first well and has no way to do the second on its own.\n\nAgentic AI wires the two together. You describe the goal (\"dock this ligand into the ATP pocket\", \"design a binder for this epitope\"), and MARA chooses which of the 300+ tools to call, in what order, with what inputs. It fires [docking engines, folding models and electrostatics solvers](https:\u002F\u002Fnanome.ai\u002Fagents) along with the cheminformatics routines around them, then hands back structures you can turn over in 3D. The day-to-day shape of that job gets walked through in [an AI copilot for drug discovery workflows](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fan-ai-copilot-for-drug-discovery-workflows).\n\nEvery step stays on the record. MARA names each tool it called, the inputs it passed, and what came back, so a finished dock arrives with its scoring function and its poses, and every line of the summary has a run behind it you can open.\n\n## What MARA can run\n\n\u003Ctable class=\"table\">\n  \u003Cthead>\n    \u003Ctr>\u003Cth>Task\u003C\u002Fth>\u003Cth>What the agent does\u003C\u002Fth>\u003Cth>Example tools\u003C\u002Fth>\u003C\u002Ftr>\n  \u003C\u002Fthead>\n  \u003Ctbody>\n    \u003Ctr>\u003Ctd>Docking\u003C\u002Ftd>\u003Ctd>Places a ligand in a pocket and scores the poses\u003C\u002Ftd>\u003Ctd>Smina, DiffDock-L, \u003Ca href=\"https:\u002F\u002Fvina.scripps.edu\">Vina\u003C\u002Fa>-style engines\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>Co-folding\u003C\u002Ftd>\u003Ctd>Folds a protein and a ligand together to predict the bound complex\u003C\u002Ftd>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fjwohlwend\u002Fboltz\">Boltz-2\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-024-07487-w\">AlphaFold 3\u003C\u002Fa>, Protenix\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>De novo binder design\u003C\u002Ftd>\u003Ctd>Generates backbones and sequences for a target pocket or epitope\u003C\u002Ftd>\u003Ctd>RFdiffusion3 (beta), ProteinMPNN\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>Electrostatics\u003C\u002Ftd>\u003Ctd>Solves and maps surface charge\u003C\u002Ftd>\u003Ctd>APBS\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>ADMET\u003C\u002Ftd>\u003Ctd>Estimates absorption, distribution, metabolism, excretion and toxicity\u003C\u002Ftd>\u003Ctd>ADMET models, eToxPred\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>Antibody annotation\u003C\u002Ftd>\u003Ctd>Numbers variable domains and defines CDR loops with ANARCI, then designs new sequences with ProteinMPNN\u003C\u002Ftd>\u003Ctd>ANARCI, ProteinMPNN\u003C\u002Ftd>\u003C\u002Ftr>\n  \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\nThe built-in library spans 300+ tools across 26 categories, and a team's own code can join it. A script or a model a group already trusts becomes a MARA tool, and from then on anyone in the org calls it by asking for it in a sentence.\n\nTwo runs show the shape of this.\n\n**A single job.** You load a protein from RCSB PDB and a ligand from PubChem, then ask for a dock. MARA finds the pockets, sets a box, runs the job, and returns ranked poses with scores. Nanome puts the top pose in front of you, in the web app or in a headset, at whatever size you want to look at it.\n\n**A chained job.** You point at an epitope and ask for new binders. RFdiffusion3 generates backbones, ProteinMPNN designs sequences onto them, and the candidates go to a folding model to see which ones hold their shape. Three tools, one request, and every intermediate structure stays in the workspace.\n\n## Behind your firewall\n\n![A flat vector diagram of a building outline containing a protein shape and a pipeline icon, illustrating that data and tool runs stay inside a secure network boundary.](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fagentic_ai_for_computational_chemistry_image_2_31a84a3395.png)\n\n\n\nNanome enterprise deployments sit inside your own network, [as a single tenant in the cloud or on hardware you run](https:\u002F\u002Fnanome.ai\u002Fmara-enterprise). Structures, prompts and tool outputs stay there. On an unpublished target that constraint decides whether an agent is usable at all, so the pipeline runs where the data already lives.\n\nNanome also ships MCP servers, a REST API and a Claude Code Skill, so the tool an analyst calls in a sentence can be called from a script too. That wiring has its own write-up in [MCP servers for cheminformatics and drug discovery](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fmcp-servers-for-cheminformatics-and-drug-discovery).\n\n## Where other tools fit\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\u002Fagentic_ai_for_computational_chemistry_image_4_bb3229892e.png\" alt='Schrödinger Maestro' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fagentic_ai_for_computational_chemistry_image_5_1c25585668.png\" alt='PyMOL' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fagentic_ai_for_computational_chemistry_image_7_702d77a3cd.png\" alt='VMD' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fagentic_ai_for_computational_chemistry_image_8_2c09039bec.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\u002Fagentic_ai_for_computational_chemistry_image_9_76a4c0faf4.png\" alt='GROMACS' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003C\u002Fdiv>\n\n\n\n![Two colleagues in a lounge area review a protein ribbon structure on a large display, one pointing at the fold with a stylus.](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fagentic_ai_for_computational_chemistry_image_3_1b33683e02.png)\n\n\n\nNanome sits alongside the comp-chem suites a group already runs. Several of them are direct integrations.\n\n\u003Ctable class=\"table\">\n  \u003Cthead>\n    \u003Ctr>\u003Cth>Tool\u003C\u002Fth>\u003Cth>Strength\u003C\u002Fth>\u003Cth>How Nanome connects\u003C\u002Fth>\u003C\u002Ftr>\n  \u003C\u002Fthead>\n  \u003Ctbody>\n    \u003Ctr>\n      \u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.schrodinger.com\u002Fplatform\u002Fproducts\u002Fmaestro\u002F\">Schrödinger Maestro\u003C\u002Fa> \u002F LiveDesign\u003C\u002Ftd>\n      \u003Ctd>Physics-based modeling at depth, plus enterprise data\u003C\u002Ftd>\n      \u003Ctd>Nanome imports Maestro \u003Ccode>.mae\u003C\u002Fcode> \u002F \u003Ccode>.maegz\u003C\u002Fcode> files (also the LiveDesign ingestion format) and integrates with \u003Ca href=\"https:\u002F\u002Fnanome.ai\u002Fintegrations\">LiveDesign\u003C\u002Fa>. MARA adds plain-English orchestration on top, and the review happens in 3D. \u003Ca href=\"https:\u002F\u002Fdocs.nanome.ai\u002Fnanome_web\u002Ffileformats\">More on supported formats\u003C\u002Fa>\u003C\u002Ftd>\n    \u003C\u002Ftr>\n    \u003Ctr>\n      \u003Ctd>\u003Ca href=\"https:\u002F\u002Fpymol.org\">PyMOL\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.rbvi.ucsf.edu\u002Fchimerax\u002F\">ChimeraX\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.ks.uiuc.edu\u002FResearch\u002Fvmd\u002F\">VMD\u003C\u002Fa>\u003C\u002Ftd>\n      \u003Ctd>Scriptable desktop visualization\u003C\u002Ftd>\n      \u003Ctd>PyMOL \u003Ccode>.pse\u003C\u002Fcode> sessions import for viewing. PDB, mmCIF, SDF, MOL2, PQR and XYZ load as structures you can edit. Molecules come back out as PDB, SDF or SMILES, one frame at a time. Multiplayer sessions and XR are the part Nanome adds. \u003Ca href=\"https:\u002F\u002Fdocs.nanome.ai\u002Fnanome_web\u002Ffileformats\">More on supported formats\u003C\u002Fa>\u003C\u002Ftd>\n    \u003C\u002Ftr>\n    \u003Ctr>\n      \u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.collaborativedrug.com\">CDD Vault\u003C\u002Fa>\u003C\u002Ftd>\n      \u003Ctd>Registration and assay data management\u003C\u002Ftd>\n      \u003Ctd>Nanome connects to \u003Ca href=\"https:\u002F\u002Fnanome.ai\u002Fblog\u002Fcollaborative-drug-discovery-and-nanome-partnership-announcement\">CDD Vault\u003C\u002Fa>, so an agent run lands next to the assay record it belongs with\u003C\u002Ftd>\n    \u003C\u002Ftr>\n  \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\nThe payoff lands on runs that cross several of those tools at once. Each step stays visible, and the people who have to agree on the result can look at it together in one 3D space.\n\nAt UC San Diego, Prof. Zoran Radić's group used Nanome across a compound library designed to treat nerve agent poisoning, working from X-ray structures through lead optimization, and published the work in the Journal of Biological Chemistry. Radić describes the loop this way: \"we can form covalent conjugates, we can minimize, dock it and score it in real time.\"\n\nWhat other groups have done with it is at [nanome.ai\u002Fcase-studies](https:\u002F\u002Fnanome.ai\u002Fcase-studies).\n\n## FAQ\n\n**What separates an agentic AI from a chemistry chatbot?**\nA chatbot returns a description of a method. MARA runs the method: it plans the task, picks tools from the 300+ in the library, executes them against your structures, and reports what each one did.\n\n**Can AI agents for drug discovery run real docking and folding?**\nYes. Docking with Smina and DiffDock-L, co-folding with Boltz-2, AlphaFold 3 and Protenix, de novo binder design with RFdiffusion3 and ProteinMPNN, electrostatics with APBS, and ADMET estimates. Each run reports the inputs it took and the results it produced.\n\n**Where does my data go?**\nNanome enterprise deployments run inside your own network, as a single tenant in the cloud or on hardware you own, so targets, prompts and results stay under your control.\n\n**Can I drive the agent from my own code?**\nYes. MCP servers, a REST API and a Claude Code Skill put the same tools in reach of your scripts, so a step you automate once behaves the same whether a script calls it or somebody asks for it in plain English.\n","2026-07-15T01:23:49.270Z","2026-09-03T16:00:23.195Z","2026-09-03T16:00:22.953Z","2026-09-03","Agentic AI for computational chemistry: how Nanome's MARA plans and runs real scientific tools from plain English, behind your firewall.","agentic AI for computational chemistry, AI agents for drug discovery, MARA, Nanome, docking, co-folding, de novo binder design","agentic-ai-for-computational-chemistry","frequent-topics",{"pagination":19},{"page":20,"pageSize":21,"pageCount":20,"total":20},1,100,{"data":23,"meta":411},[24,29,35,40,45,50,55,60,65,70,75,81,86,92,97,102,107,112,117,122,127,132,137,142,147,153,158,163,168,173,178,183,188,193,198,203,208,213,218,223,228,233,238,243,248,253,258,263,268,273,278,283,288,293,298,303,309,314,319,324,329,334,339,344,349,354,359,364,369,374,379,384,389,394,399,404,406],{"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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