[{"data":1,"prerenderedAt":492},["ShallowReactive",2],{"blog-post-spatial-computing-for-pharmaceutical-research":3,"blog-posts-nav":21},{"data":4,"meta":18},[5],{"id":6,"attributes":7},100,{"title":8,"content":9,"createdAt":10,"updatedAt":11,"publishedAt":12,"date":13,"description":14,"keywords":15,"slug":16,"category":17},"Spatial computing for pharmaceutical research","Spatial computing for pharmaceutical research means viewing and manipulating molecular structures as true 3D objects in immersive space, then working on them with your team as if you were standing around the same physical model. Nanome is one platform pharma teams use for this. It's a collaborative molecular visualization and drug discovery platform that runs on XR headsets, Windows desktop, and a browser web app. Nanome's AI copilot, [MARA](https:\u002F\u002Fnanome.ai\u002Fmara), runs the analysis tools.\n\nA protein has depth, and a headset hands that depth back. Someone can walk around the structure at whatever scale suits the question, put a hand into the binding pocket, and turn it while a colleague standing opposite watches the same atoms move. The headsets in play are [Meta Quest, HTC Vive Focus 3, Apple Vision Pro and Pico Neo, with a Windows app and a browser for anyone without one](https:\u002F\u002Fnanome.ai\u002Fsetup).\n\n## What spatial computing changes in R&D\n\n![A researcher wearing a slim VR headset turns a ribbon-cartoon protein structure floating at chest height in an open studio space, both hands engaged with the model.](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fspatial_computing_for_pharmaceutical_research_image_1_r3_f0b4225b1c.png)\n\n\n\nDepth carries most of the argument. On a flat screen, shading and rotation stand in for the third dimension, and the reconstruction happens in the head of whoever is looking. Give the structure real volume and real scale and that step drops out. A pocket reads as a cavity with room in it, and a bad contact sitting behind a side chain is visible on the first turn.\n\nPresence is the second change. Two scientists on opposite coasts stand in one room and argue about a docked pose that sits in front of both of them, which is the working pattern [collaborative drug discovery software for remote teams](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fcollaborative-drug-discovery-software-for-remote-teams) goes through in detail.\n\nImmersive space is doing interface work here. It puts a person at the scale the chemistry happens on, which is where molecular design has been short for a while.\n\nOn an org chart, spatial computing turns up in the review meeting, where the shared view is the object a chemist, a biologist and a modeler can all point at, and [what a modern molecular presentation looks like](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fwhat-a-modern-molecular-presentation-looks-like) covers how that session gets built.\n\n## Where the tools come in\n\nA session opens on the real thing. A structure arrives by accession code from RCSB PDB, PubChem, DrugBank, ChEMBL or UniProt, or straight off a disk. Seeing it settles some questions; whether a compound binds, folds, or trips a toxicity flag takes a calculation.\n\nThat half goes to MARA, Nanome's AI copilot, which carries [300+ integrated scientific tools](https:\u002F\u002Fnanome.ai\u002Fintegrations) spread over 26 categories. Ask for one in plain English and the result lands back in the same 3D scene. With a headset on, the request can be spoken out loud instead of typed, which keeps both hands on the molecule.\n\nSome of the ground it covers: docking with Smina or DiffDock-L; folding and co-folding through [AlphaFold 3](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-024-07487-w), [Boltz-2](https:\u002F\u002Fgithub.com\u002Fjwohlwend\u002Fboltz), OpenFold3 and Chai-1; electrostatics with APBS; ADMET and toxicity models; ProteinMPNN for sequence design, with ANARCI numbering antibody variable domains and marking their CDR loops; and RFdiffusion3 (beta) for de novo binders.\n\nEach run leaves a record of the tool, what went into it and what came out, so a number on a slide can be walked back to the job behind it. Once a pose is seated, [how to analyze protein-ligand interactions](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fhow-to-analyze-protein-ligand-interactions) covers the measurements that follow.\n\n## How Nanome compares to other visualization tools\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\u002Fspatial_computing_for_pharmaceutical_research_image_4_7e4d45002b.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\u002Fspatial_computing_for_pharmaceutical_research_image_5_08ccd52555.png\" alt='UCSF ChimeraX' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003Cimg src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fspatial_computing_for_pharmaceutical_research_image_6_8767392b4b.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\u002Fspatial_computing_for_pharmaceutical_research_image_7_9de7e5b8bc.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\u002Fspatial_computing_for_pharmaceutical_research_image_8_5776041dfb.png\" alt='MOE' style=\"width:120px;height:44px;object-fit:contain;display:inline-block\">\u003C\u002Fdiv>\n\n\n\n\u003Ctable class=\"table\">\n  \u003Cthead>\n    \u003Ctr>\u003Cth>Tool\u003C\u002Fth>\u003Cth>Its strength\u003C\u002Fth>\u003Cth>What immersive space adds\u003C\u002Fth>\u003C\u002Ftr>\n  \u003C\u002Fthead>\n  \u003Ctbody>\n    \u003Ctr>\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>\u003Ctd>Deep scripting and rendering on the desktop, one operator at a time\u003C\u002Ftd>\u003Ctd>A structure at body scale that a group holds together\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.schrodinger.com\u002Fplatform\u002Fproducts\u002Fmaestro\u002F\">Schrödinger Maestro\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.chemcomp.com\">MOE\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fbiovia\u002Fdiscovery-studio\">BIOVIA Discovery Studio\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>Full comp-chem suites for the modeling itself\u003C\u002Ftd>\u003Ctd>Nanome connects to several of them (\u003Ca href=\"https:\u002F\u002Fnanome.ai\u002Fintegrations\">Schrödinger LiveDesign\u003C\u002Fa>) and puts their output in a room\u003C\u002Ftd>\u003C\u002Ftr>\n    \u003Ctr>\u003Ctd>Web-only molecular viewers\u003C\u002Ftd>\u003Ctd>Fast structure lookups in a browser tab\u003C\u002Ftd>\u003Ctd>Nanome's web app covers that, then carries the same session into a headset\u003C\u002Ftd>\u003C\u002Ftr>\n  \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\nNanome imports PyMOL `.pse` sessions, along with the structure files those desktop tools write every day: PDB, mmCIF, SDF, MOL and MOL2, XYZ and PQR. From the Schrödinger side it takes Maestro `.mae` and `.maegz`, the format LiveDesign ingests, and it reads `.moe` files from MOE. [More on supported formats](https:\u002F\u002Fdocs.nanome.ai\u002Fnanome_web\u002Ffileformats).\n\nWhatever pipeline a group already runs can stay put. Nanome connects to [Schrödinger](https:\u002F\u002Fwww.schrodinger.com) LiveDesign, Cresset Flare, [OpenEye \u002F Cadence](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fview-your-openeyecadence-data-with-fresh-eyes-using-nanome-xr), [CDD Vault](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fcollaborative-drug-discovery-and-nanome-partnership-announcement), [KNIME](https:\u002F\u002Fwww.knime.com), Jupyter, and the [OpenFold Consortium](https:\u002F\u002Fnanome.ai\u002Fblog\u002Fnanome-joins-the-openfold-consortium).\n\n## Real work done this way\n\n![Two colleagues wearing ultra-thin VR headsets examine the same space-filling protein model floating between them](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fspatial_computing_for_pharmaceutical_research_image_2_v4s_899f025ad1.png)\n\n\n\nNimbus Therapeutics had settled on a selectivity strategy for the AMPKβ2 enzyme. Seeing the protein in motion in VR, the team spotted a better synthetic vector and changed course, and the compounds that came out of the new plan were more active on the target.\n\nNimbus works against a lead-optimization cycle that runs 12 to 18 months, and a course correction found in a review arrives before the chemistry gets made.\n\n## Deployment and security\n\n![A flat vector diagram shows a molecular structure and an AI model both contained inside a single building outline, with no data crossing the perimeter wall.](https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fspatial_computing_for_pharmaceutical_research_image_3_bc102c949c.png)\n\n\n\nFor internal tooling there's an open REST API, MCP servers, and a Nanome Claude Code Skill.\n\n## What a desktop tool still does better\n\nScripting a publication figure is quicker in PyMOL or ChimeraX, and for solo rendering work that's usually the better route. A calculation that already lives in a Schrödinger or MOE workflow can stay exactly where it is; Nanome takes the output and puts it in front of the group in 3D.\n\n[nanome.ai\u002Fcase-studies](https:\u002F\u002Fnanome.ai\u002Fcase-studies) collects write-ups of how different groups run this in practice.\n\n## FAQ\n\n**What is spatial computing for pharmaceutical research?**\nUsing immersive 3D and XR headsets to view, manipulate, and collaborate on molecular structures as real spatial objects. Nanome is one platform pharma teams use for it, on Pico Neo, HTC Vive Focus 3, Meta Quest and Apple Vision Pro, and on Windows desktop and in a browser.\n\n**Do I need a headset to use Nanome?**\nNo. The browser web app runs without one, and there's a Windows desktop app as well. Full immersion needs a headset; loading structures and running MARA tools does not.\n\n**Does Apple Vision Pro work with Nanome?**\nYes. Apple Vision Pro is supported, and so are the Quest, Focus 3 and Neo headsets.\n\n**What file formats can Nanome open?**\nStructures first: PDB (`.pdb`, `.ent`), SDF, MOL and MOL2, mmCIF (`.cif`, `.mmcif`, `.bcif`), SMILES, XYZ, PQR, and PDBQT, which comes in as PDB with its charges dropped. Vendor and session files load too: Maestro `.mae` and `.maegz`, MOE `.moe`, and PyMOL `.pse`. An electrostatic map (`.dx`) overlays a structure that's already open. Coming back out, a molecule saves as PDB, SDF or SMILES, and only the frame on screen travels with it, which leaves mmCIF, MAE, MOE and PSE on the import side. The full table sits in [the format docs](https:\u002F\u002Fdocs.nanome.ai\u002Fnanome_web\u002Ffileformats).\n\n**Can spatial computing run real drug discovery calculations, or is it only visualization?**\nBoth. Through MARA, Nanome reaches 300+ tools covering docking, co-folding, ADMET prediction, binder design and structure prediction, with results returned into the same 3D scene and a record of every job that ran.\n","2026-07-15T01:23:52.068Z","2026-09-17T16:00:06.711Z","2026-09-17T16:00:06.651Z","2026-09-17","Spatial computing for pharmaceutical research: Nanome runs immersive 3D structure review and MARA tools across headsets and the web.","spatial computing for pharmaceutical research, pharma spatial computing, Apple Vision Pro drug discovery, immersive molecular visualization, Nanome, MARA, XR drug discovery","spatial-computing-for-pharmaceutical-research","frequent-topics",{"pagination":19},{"page":20,"pageSize":6,"pageCount":20,"total":20},1,{"data":22,"meta":490},[23,28,34,39,44,49,54,59,64,69,74,80,85,90,96,101,106,111,116,121,126,131,136,141,146,152,157,162,167,172,177,182,187,192,197,202,207,212,217,222,227,232,237,242,247,252,257,262,267,272,277,282,287,292,297,302,308,313,318,323,328,333,338,343,348,353,358,363,368,373,378,383,388,393,398,403,408,413,418,423,428,433,438,443,448,453,458,463,468,473,475,480,485],{"id":20,"attributes":24},{"slug":25,"title":26,"category":27},"spy-stories-and-rational-drug-design","Spy stories & rational drug design","case-studies",{"id":29,"attributes":30},2,{"slug":31,"title":32,"category":33},"meta-quest-pro-and-a-new-version-of-nanome-(v1.24)","Meta Quest Pro & a new version of Nanome (v1.24)","releases",{"id":35,"attributes":36},3,{"slug":37,"title":38,"category":27},"beating-the-pandemic-in-virtual-reality","Beating the Pandemic in Virtual Reality",{"id":40,"attributes":41},4,{"slug":42,"title":43,"category":27},"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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