[{"data":1,"prerenderedAt":338},["ShallowReactive",2],{"blog-post-how-resonac-turned-two-years-of-misaligned-randd-into-a-single-afternoon-breakthrough":3,"blog-posts-nav":22},{"data":4,"meta":18},[5],{"id":6,"attributes":7},140,{"title":8,"content":9,"createdAt":10,"updatedAt":11,"publishedAt":12,"date":13,"description":14,"keywords":15,"slug":16,"category":17},"How Resonac Turned Two Years of Misaligned R&D Into a Single Afternoon Breakthrough","\u003Cdiv style=\"overflow: hidden; display: inline-block; margin: 0px 0px; width: 720px; height: 540px;\">\n  \u003Cimg alt=\"how resonac turned two years of misaligned randd into a single afternoon breakthrough image1\" src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fhow_resonac_turned_two_years_of_misaligned_randd_into_a_single_afternoon_breakthrough_image1_ec90444942.jpg\" style=\"width: 720px; height: 540px;\">\n\u003C\u002Fdiv>\n\n\u003Cp>Computational chemistry teams generate extraordinary insights. But insights that don't reach the rest of the organization might as well not exist. Resonac found a way to close that gap, and the results compressed months of experimental iteration into days.\u003C\u002Fp>\n\n\u003Ch2>The Challenge Every Discovery Organization Recognizes\u003C\u002Fh2>\n\n\u003Cp>In most R&D organizations, computational and experimental teams operate with different tools, different outputs, and different intuitions. Computational chemists work in trajectories, energy landscapes, and numerical predictions. Experimental chemists work in flasks, assays, and physical observations. Both groups are rigorous. Both are essential. But they're often looking at the same science through completely different lenses.\u003C\u002Fp>\n\n\u003Cp>This was exactly the situation Resonac's teams found themselves in.\u003C\u002Fp>\n\n\u003Ch2>The Science\u003C\u002Fh2>\n\n\u003Cp>Resonac's research focused on identifying additives that improve the stability of a vitamin C derivative used in cosmetic formulations. The target compound, Trisodium Ascorbyl Palmitate Phosphate (APPS), is designed to enhance the bioavailability of vitamin C, but its ester bonds can undergo hydrolysis in aqueous solution. The standard approach has been to add stabilizing agents, though the molecular mechanisms behind why certain stabilizers work better than others have not been well understood. Historically, the search for effective additives relied heavily on empirical testing and formulator intuition.\u003C\u002Fp>\n\n\u003Cp>The computational team set out to change that. Using GROMACS with the GAFF2 force field, they ran molecular dynamics simulations of APPS in aqueous solution with dozens of stabilizers under consideration, several of which are introduced here, including ethanol, hexanol, lauryl alcohol, cetanol, and behenyl alcohol. Each system contained roughly 200 APPS molecules, 150,000 water molecules, counterions, and the candidate additive, all at concentrations matching real formulation conditions. Charge distributions were calculated using Gaussian16 at the HF\u002F6-31+g(d) level before the dynamics runs began.\u003C\u002Fp>\n\n\u003Cdiv style=\"overflow: hidden; display: inline-block; margin: 0px 0px; width: 645px; height: 370px;\">\n  \u003Cimg alt=\"resonac molecular dynamics simulation setup image2\" src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fhow_resonac_turned_two_years_of_misaligned_randd_into_a_single_afternoon_breakthrough_image2_9560da71e0.png\" style=\"width: 645px; height: 370px;\">\n\u003C\u002Fdiv>\n\n\u003Cp>\u003Cem>Pictured: Resonac's molecular dynamics simulation setup. The table (top left) shows five fatty alcohol additives tested alongside APPS in aqueous solution at formulation-relevant concentrations. Below, the molecular structures and system composition used in the GROMACS simulations: ~200 APPS molecules, 150,000 water molecules, counterions, and the candidate additive. Figure courtesy of Resonac Corporation.\u003C\u002Fem>\u003C\u002Fp>\n\n\u003Cp>The simulations ran through a multi-stage equilibration protocol, progressing from overlap removal through NVT and NPT relaxation phases, ultimately producing production trajectories at realistic temperatures and pressures. What emerged from those trajectories told a clear story: different additives produced structurally distinct outcomes. Lauryl alcohol molecules aggregated around APPS to form micelle-like structures, with the additive and APPS clustering together while water was excluded from the interior. Behenyl alcohol, by contrast, drove the formation of layered lamellar structures, a fundamentally different packing arrangement. The shorter-chain alcohols like ethanol produced far less organized aggregation.\u003C\u002Fp>\n\n\u003Cp>These structural differences matter because they directly determine how well each additive shields the vulnerable ester bonds from hydrolysis. A tightly packed micelle buries the reactive sites inside. A loose, disorganized cluster leaves them exposed. The simulations gave the computational team a molecular-level explanation for performance differences that had previously only been observed empirically, and pointed toward which structural features to optimize in future additive candidates.\u003C\u002Fp>\n\n\u003Cp>The findings were clear in the data. But translating molecular dynamics outputs into something the experimental team could act on proved to be the real challenge. The evidence lived in trajectories, energy calculations, and 2D projections of inherently three-dimensional phenomena. The format was precise, but it didn't convey the spatial reality of what was actually happening at the molecular level. The difference between a micelle and a lamellar phase is immediately obvious when you can see both structures in three dimensions. On a flat screen, reduced to plots and cross-sections, that distinction collapses.\u003C\u002Fp>\n\n\u003Cp>The experimental team, rightly cautious about changing course based on simulation outputs alone, continued with their established approach. This wasn't a disagreement about the science. It was a limitation of the medium available to communicate it. For nearly two years, both teams operated in parallel: the computational side confident in their predictions, the experimental side waiting for evidence in a format that matched their intuition.\u003C\u002Fp>\n\n\u003Ch2>What Changed\u003C\u002Fh2>\n\n\u003Cdiv style=\"overflow: hidden; display: inline-block; margin: 0px 0px; width: 719px; height: 539px;\">\n  \u003Cimg alt=\"resonac team using nanome for collaborative molecular dynamics review image3\" src=\"https:\u002F\u002Fnanome-cms.s3.us-west-1.amazonaws.com\u002Fhow_resonac_turned_two_years_of_misaligned_randd_into_a_single_afternoon_breakthrough_image3_9f316d55a9.jpg\" style=\"width: 719px; height: 539px;\">\n\u003C\u002Fdiv>\n\n\u003Cp>\u003Cem>Pictured: Resonac's computational and experimental teams reviewing molecular dynamics trajectories together in Nanome. Photo courtesy of Resonac Corporation.\u003C\u002Fem>\u003C\u002Fp>\n\n\u003Cp>Resonac's computational team loaded their molecular dynamics trajectories into Nanome and set up a collaborative review session with the experimental team.\u003C\u002Fp>\n\n\u003Cp>The same data that had lived in spreadsheets and 2D plots was now something both teams could explore together in three dimensions. The aggregation behavior the simulations had predicted became spatially visible: molecules clustering, packing together, forming structures that would crash out of solution. Instead of interpreting numbers on a page, the experimental team could rotate the system, zoom into problem areas, and develop their own spatial intuition for what the simulations were showing.\u003C\u002Fp>\n\n\u003Cp>Within a few hours, both teams were aligned. The experimental team could see exactly what the computational team had been flagging, and they had the context to act on it with confidence. They adjusted their experimental approach accordingly, and the results validated the computational predictions.\u003C\u002Fp>\n\n\u003Ch2>The Results\u003C\u002Fh2>\n\n\u003Cp>The impact showed up on two levels.\u003C\u002Fp>\n\n\u003Cp>First, what had been a six-month experimental iteration cycle collapsed to roughly two to three days. The combination of molecular dynamics simulation and real-time collaborative review in Nanome eliminated the back-and-forth that typically stretches discovery timelines.\u003C\u002Fp>\n\n\u003Cp>Second, and more significantly, Resonac broke through a communication barrier that had persisted for two years. Not because anyone was wrong, but because the right tool finally existed to put computational and experimental chemists on the same page. Literally on the same screen, looking at the same rotating structure, building a shared understanding of the science.\u003C\u002Fp>\n\n\u003Ch2>Why This Matters Beyond Resonac\u003C\u002Fh2>\n\n\u003Cp>Every pharma and materials science organization wrestles with the same translation problem. Computational teams produce outputs that are numerically precise but spatially abstract. Experimental teams need physical intuition before they'll change course, and that's exactly the rigor you want from the people running your experiments.\u003C\u002Fp>\n\n\u003Cp>The gap between insight and action isn't about one group being right and another being slow to catch up. It's about the medium. Tables and 2D projections flatten three-dimensional phenomena into formats that strip away the spatial context chemists rely on to make decisions.\u003C\u002Fp>\n\n\u003Cp>Resonac's experience shows what happens when you restore that context. Two years of parallel effort converged in a single afternoon. Not because the data changed, but because everyone could finally see the same thing at the same time.\u003C\u002Fp>\n\n\u003Chr>\n\n\u003Cp>\u003Cem>Resonac is a global chemical and materials company headquartered in Tokyo. Their R&D teams use Nanome to bridge computational and experimental workflows through collaborative 3D molecular visualization.\u003C\u002Fem>\u003C\u002Fp>\n\n\u003Cp>\u003Cem>\u003Ca href=\"https:\u002F\u002Fnanome.ai\">Nanome\u003C\u002Fa> is a collaborative platform for 3D molecular visualization, available on web and XR. Teams use it to review computational results, annotate structures, and make faster decisions together.\u003C\u002Fem>\u003C\u002Fp>\n\n\u003Chr>\n","2026-07-27T21:15:14.965Z","2026-07-28T04:39:37.000Z","2026-07-28T04:39:36.992Z","2026-07-28","Resonac's computational and experimental teams spent two years misaligned—until one collaborative 3D session in Nanome collapsed a 6-month cycle into days.","Resonac, molecular dynamics, computational chemistry, R&D collaboration, Nanome, 3D molecular visualization, APPS stabilization, vitamin C derivative, drug discovery, materials science","how-resonac-turned-two-years-of-misaligned-randd-into-a-single-afternoon-breakthrough","case-studies",{"pagination":19},{"page":20,"pageSize":21,"pageCount":20,"total":20},1,100,{"data":23,"meta":335},[24,28,34,39,44,49,54,59,64,69,74,80,85,91,96,101,106,111,116,121,126,131,136,141,146,152,157,162,167,172,177,182,187,192,197,202,207,212,217,222,227,232,237,242,247,252,257,262,267,272,277,282,287,292,297,302,308,313,318,323,328,333],{"id":20,"attributes":25},{"slug":26,"title":27,"category":17},"spy-stories-and-rational-drug-design","Spy stories & rational drug design",{"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":17},"beating-the-pandemic-in-virtual-reality","Beating the Pandemic in Virtual Reality",{"id":40,"attributes":41},4,{"slug":42,"title":43,"category":17},"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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