{"id":455,"date":"2026-01-29T04:35:31","date_gmt":"2026-01-29T04:35:31","guid":{"rendered":"https:\/\/www.boltzmannmaps.com\/blog\/?p=455"},"modified":"2026-01-29T04:35:33","modified_gmt":"2026-01-29T04:35:33","slug":"deep-learning-docking-scores-with-gnina-now-in-boltzmann-maps","status":"publish","type":"post","link":"https:\/\/www.boltzmannmaps.com\/blog\/deep-learning-docking-scores-with-gnina-now-in-boltzmann-maps\/","title":{"rendered":"Deep Learning Docking Scores with Gnina Now in Boltzmann Maps"},"content":{"rendered":"\n<p>We\u2019ve just added <strong><a href=\"https:\/\/github.com\/gnina\/gnina\">Gnina-based scoring<\/a><\/strong> (pronounced NEE-na) [<a href=\"https:\/\/link.springer.com\/article\/10.1186\/s13321-021-00522-2\">Publication<\/a>] to Boltzmann Maps \u2014 giving you another way to evaluate docking poses, side-by-side with tools like <a href=\"https:\/\/www.boltzmannmaps.com\/blog\/diffdock-implemented-in-boltzmann-maps\/\">Vina<\/a>, <a href=\"https:\/\/www.boltzmannmaps.com\/blog\/diffdock-implemented-in-boltzmann-maps\/\">DiffDock<\/a>, BMaps, and <a href=\"https:\/\/www.boltzmannmaps.com\/blog\/compound-energy-minimization-with-openmm\/\">OpenMM<\/a>.<\/p>\n\n\n\n<p>Gnina extends the Vina\/Smina framework with deep-learning models trained on experimental protein\u2013ligand complexes. Instead of relying only on force-field heuristics, it predicts binding affinity from the 3D structure of a complex \u2014 using a CNN trained to recognize patterns seen in real-world binders.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Add Gnina?<\/strong><\/h2>\n\n\n\n<p>It helps you <strong>rank<\/strong>, <strong>cross-check<\/strong>, and <strong>triage<\/strong> poses with an extra layer of insight.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Rescoring:<\/strong> Use Gnina to prioritize realistic poses from any method \u2014 Vina, DiffDock, or your own.<\/li>\n\n\n\n<li><strong>Pose selection:<\/strong> If both Gnina and your docking method favor the same pose, you have a stronger case to move it forward.<\/li>\n\n\n\n<li><strong>Hit enrichment:<\/strong> Combine Gnina with OpenMM minimization to pull the most plausible binders to the top of your list.<\/li>\n<\/ul>\n\n\n\n<p>Gnina doesn\u2019t replace traditional scores \u2014 it complements them. And in Boltzmann Maps, you can see them all in one place.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Use Cases Inside BMaps<\/strong><\/h2>\n\n\n\n<p>With Gnina energies now built into Boltzmann Maps, you can:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>View Gnina scores alongside docking scores, minimization energy, fragment maps, and water networks.<\/li>\n\n\n\n<li>Run scoring on any pose set \u2014 from DiffDock, Vina, or elsewhere.<\/li>\n\n\n\n<li>Filter, rank, and prioritize analogs or fragments with multiple scoring channels.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Example: Using Gnina Metrics to Gain Confidence and Find New Fragment Growing Opportunities<\/strong><\/h2>\n\n\n\n<p>Here we are looking at different poses of the ligand 3TE bound to the sirtuin-2 protein 4RMG. Six poses were found using DiffDock and minimized within the protein environment using the OpenMM minimizer supported in BMaps and scored according to the BMaps interaction score calculator. These are labeled 3TE_[1-6], whereas the crystal bound ligand is 3TE.1:X<\/p>\n\n\n\n<p>As our goal is to determine if there are any poses we may prefer to analyze over or alongside the crystal ligand pose, we turn to the Compound Table where we can compare calculated values for each compound. Rapid Gnina energy calculations are run automatically in the background and are ready by the time we open the Compound Table.<\/p>\n\n\n\n<p>In the Compound Table we compare the Interaction Score vs three Gnina calculated values:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Gnina Pose Score<\/strong> &#8211; Gnina&#8217;s predicted probability that the pose is within 2\u00c5 of the correct binding pose [Best Value: 1.0]<\/li>\n\n\n\n<li><strong>Gnina Affinity<\/strong> &#8211; Gnina&#8217;s prediction of binding affinity in pK units (6 = 1 \u00b5M, 9 = 1 nM) [Higher is better]<\/li>\n\n\n\n<li><strong>Gnina Stress<\/strong> &#8211; Intramolecular energy calculated by Gnina [More negative is better]<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"398\" src=\"https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-2-1024x398.png\" alt=\"Figure 1. Compound Table in Boltzmann Maps showing metrics for comparison against the crystal ligand and 6 different DiffDock derived and BMaps minimized poses.\" class=\"wp-image-458\" style=\"width:840px;height:auto\" srcset=\"https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-2-1024x398.png 1024w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-2-300x117.png 300w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-2-768x299.png 768w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-2.png 1507w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Figure 1. <\/strong>Compound Table in Boltzmann Maps showing metrics for comparison against the crystal ligand and 6 different DiffDock derived and BMaps minimized poses.<\/p>\n\n\n\n<p>From this we are instantly convinced that our crystal pose is the best pose we have been able to find as all four metrics align to show this. In addition, we see that 3TE_1 is pretty close in metrics across the board as well, so if its pose is unique, it may provide additional binding site or binding mode opportunities. Note that because we used DiffDock to perform docking and as DiffDock is a global protein docker, the docked poses may be in different sites, not just the same site as the crystal pose.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"578\" height=\"682\" src=\"https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-4.png\" alt=\"Figure 2. Ligand View of 4RMG PDB in BMaps with NAD (grey), crystal ligand 3TE.1:X in green and docked and minimized pose 3TE_1 in light blue. 3TE_1 naphthalene is rotated towards NAD and is exposed to additional pocket space as compared to the crystal ligand naphthalene.\" class=\"wp-image-459\" srcset=\"https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-4.png 578w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-4-254x300.png 254w\" sizes=\"(max-width: 578px) 100vw, 578px\" \/><\/figure><\/div>\n\n\n<p><strong>Figure 2.<\/strong> Ligand View of 4RMG PDB in BMaps with NAD (grey), crystal ligand 3TE.1:X in green and docked and minimized pose 3TE_1 in light blue. 3TE_1 naphthalene is rotated towards NAD and is exposed to additional pocket space as compared to the crystal ligand naphthalene.<\/p>\n\n\n\n<p>Looking at the two ligand poses, 3TE.1:X our crystal pose in green, and 3TE_1 our docked pose in light blue, we see that the difference between their poses is almost entirely the rotation of the dihedral between the 1,3-thiazole and the naphthalene. While the crystal pose has more of a chance to interact with the aromatic ring of the phenylalanine in SIRT-2 through a t-pose pi-pi interaction with the naphthalene, our pi-pi tool indicates that interaction is not currently occurring. Meanwhile, the 3TE_1 naphthalene pose comes much closer to forming a good hydrogen bonding angle with the hydroxyls of the interacting NAD and also gives us better access to open areas in the pocket for potential fragment growing. Indeed a fragment grow in the now accessible pocket reveals 26 fragments with binding scores that may improve the overall ligand affinity.<\/p>\n\n\n\n<p>Comparing this newly grown fragment in the Compound Table against our crystal ligand, we see we have improved across Interaction Score and Gnina Affinity, while Gnina Pose Score and Gnina Stress indicates to us that this structure is significantly more stressed than the crystal ligand which may affect its binding stability in the pocket. We also notice that this structure is stabilized by two new pi-pi interactions with the protein which may help to overcome the penalty from the stress and also indicate a partial reason for the improved Interaction Score and Gnina Affinity.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"96\" src=\"https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-6-1024x96.png\" alt=\"Figure 3. Easy comparison across Gnina and Bmaps metrics in the BMaps Compound Table for crystal ligand 3TE.1:X and fragment grown compound from 3TE_1, here called 3TE_7. Table shows improved Interaction Score and Gnina Affinity and less good Gnina Pose Score and Gnina Stress. \" class=\"wp-image-462\" srcset=\"https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-6-1024x96.png 1024w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-6-300x28.png 300w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-6-768x72.png 768w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-6-1536x144.png 1536w, https:\/\/www.boltzmannmaps.com\/blog\/wp-content\/uploads\/2026\/01\/image-6-2048x192.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Figure 3.<\/strong> Easy comparison across Gnina and Bmaps metrics in the BMaps Compound Table for crystal ligand 3TE.1:X and fragment grown compound from 3TE_1, here called 3TE_7. Table shows improved Interaction Score and Gnina Affinity and less good Gnina Pose Score and Gnina Stress. <\/p>\n\n\n\n<p>By having the additional Gnina scoring metrics we were therefore able to gain confidence in the results of triaging alternative poses from our crystal ligand, leading to the discovery of a fragment growing opportunity to improve ligand binding. We also find the Gnina scores helpful in evaluating the results of our fragment growing opportunities to guide us towards further improvements throughout the design process as well as warn us of potential energetic liabilities that fragment grown structures may have.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Built for Comparison<\/strong><\/h2>\n\n\n\n<p>Gnina fits naturally into BMaps, and as we saw with our example, using multiple methods and comparing them directly in the Compound Table can build more trust in your results than any single score could.<\/p>\n\n\n\n<p>Here is a typical workflow:<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Dock (Vina, DiffDock, etc.)<\/li>\n\n\n\n<li>Minimize the ligand<\/li>\n\n\n\n<li>Score with Gnina \u2013 Done automatically<\/li>\n\n\n\n<li>Quickly overview results in the Energies tab<\/li>\n\n\n\n<li>Filter and sort using the Compound Table for deeper analysis<\/li>\n<\/ol>\n\n\n\n<p>This makes it easier to spot strong candidates \u2014 and weaker ones \u2014 before investing in synthesis or simulations.<\/p>\n\n\n\n<h1 class=\"wp-block-heading has-text-align-center\">Move faster and design smarter with Gnina scoring available now in Boltzmann Maps.<\/h1>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Sneak Preview:<\/strong> Gnina Docking will be available in a couple of weeks!<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>Thank you to the authors of Gnina. You can find their publication and Github repo here:<\/p>\n\n\n\n<p>McNutt, A.T., Francoeur, P., Aggarwal, R.\u00a0<em>et al.<\/em>\u00a0GNINA\u00a01.0: molecular docking with deep learning.\u00a0<em>J Cheminform<\/em>\u00a0<strong>13<\/strong>, 43 (2021). https:\/\/doi.org\/10.1186\/s13321-021-00522-2<\/p>\n\n\n\n<p><a href=\"https:\/\/github.com\/gnina\/gnina\">Gnina Github Repo<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Move faster and design smarter with Gnina scoring available now in Boltzmann Maps.<\/p>\n","protected":false},"author":12,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[32,31,5,30,28,33,29],"class_list":["post-455","post","type-post","status-publish","format-standard","hentry","category-product-news","tag-cadd","tag-computational-drug-design","tag-diffdock","tag-energy-calculation","tag-gnina","tag-medicinal-chemistry","tag-molecular-energy"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.1 - 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