{"id":999744,"date":"2024-01-30T05:21:21","date_gmt":"2024-01-30T13:21:21","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-blog-post&#038;p=999744"},"modified":"2024-06-10T10:00:42","modified_gmt":"2024-06-10T17:00:42","slug":"generative-ai-meets-structural-biology-equilibrium-distribution-prediction","status":"publish","type":"msr-blog-post","link":"https:\/\/www.microsoft.com\/en-us\/research\/articles\/generative-ai-meets-structural-biology-equilibrium-distribution-prediction\/","title":{"rendered":"Generative AI Meets Structural Biology: Equilibrium Distribution Prediction"},"content":{"rendered":"\n<p class=\"has-purple-color has-text-color has-link-color wp-elements-3b0226bbcb5bb2517e3d7f259be3027d\"><em>Presented by <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shuz\/\">Shuxin Zheng<\/a> at <strong>Microsoft Research Forum, January 2024<\/strong><\/em><\/p>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-none  has-vertical-padding-none  is-stacked-on-mobile has-white-background-color has-background\" style=\"grid-template-columns:25% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"360\" height=\"360\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/12\/\u5fae\u4fe1\u56fe\u7247_Shuxin-Zheng_360x360.jpg\" alt=\"photo of Shuxin Zheng\" class=\"wp-image-992646 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/12\/\u5fae\u4fe1\u56fe\u7247_Shuxin-Zheng_360x360.jpg 360w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/12\/\u5fae\u4fe1\u56fe\u7247_Shuxin-Zheng_360x360-300x300.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/12\/\u5fae\u4fe1\u56fe\u7247_Shuxin-Zheng_360x360-150x150.jpg 150w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/12\/\u5fae\u4fe1\u56fe\u7247_Shuxin-Zheng_360x360-180x180.jpg 180w\" sizes=\"(max-width: 360px) 100vw, 360px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<blockquote class=\"wp-block-quote is-style-spectrum is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cUnderstanding equilibrium distributions in molecular science is challenging but exciting. \u2026 By learning about the different states and the behavior of molecules, scientists can make breakthroughs in developing new drugs, creating advanced materials, and understanding biological processes.\u201d<\/p>\n<cite><em>\u2013<\/em> Shuxin Zheng, Principal Researcher<\/cite><\/blockquote>\n<\/div><\/div>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Generative AI meets Structural Biology: Equilibrium Distribution Prediction\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/TNaEicUPvrY?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/msrchat.azurewebsites.net\/?askmsr=Summarize%20the%20main%20three%20points%20of%20Shuxin%27s%20talk\" target=\"_blank\" aria-label=\"Summarize the main three points of Shuxin's talk\" data-bi-type=\"annotated-link\" data-bi-cN=\"Summarize the main three points of Shuxin's talk\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-240x135.png\" class=\"mb-2\" alt=\"Ask Microsoft research copilot experience\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-240x135.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-343x193.png 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo-1280x720.png 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/01\/MSR-Chat-Promo.png 1400w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">Microsoft research copilot experience<\/span>\n\t\t\t<a href=\"https:\/\/msrchat.azurewebsites.net\/?askmsr=Summarize%20the%20main%20three%20points%20of%20Shuxin%27s%20talk\" target=\"_blank\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Summarize the main three points of Shuxin's talk\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Summarize the main three points of Shuxin's talk\">\n\t\t\t\tSummarize the main three points of Shuxin's talk&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n<div class=\"wp-block-msr-show-more\">\n\t<div class=\"bg-neutral-100 p-5\">\n\t\t<div class=\"show-more-show-less\" data-mount=\"show-more-show-less\">\n\t\t\t<div>\n\t\t\t\t<span>\n\t\t\t\t\t\n\n<h3 class=\"wp-block-heading\" id=\"transcript\">Transcript<\/h3>\n\n\n\n<p><strong>Shuxin Zheng,<\/strong> Principal Researcher, Microsoft Research AI4Science&nbsp;<\/p>\n\n\n\n<p>Shuxin Zheng presents how his team uses generative AI to solve a long-standing challenge in structural biology and molecular science\u2014predicting equilibrium distribution for molecular systems.&nbsp;<\/p>\n\n\n\n<p><em>Microsoft Research Forum, January 30, 2024<\/em><\/p>\n\n\n\n<p><strong>SHUXIN ZHENG:<\/strong> Hi, everyone. I&#8217;m Shuxin from Microsoft Research AI4Science. Thank you for joining this exciting discussion of our latest research, called Distributional Graphormer, which uses generative AI to solve a long-standing challenge in structural biology: the prediction of equilibrium distribution.<\/p>\n\n\n\n\t\t\t\t<\/span>\n\t\t\t\t<span id=\"show-more-show-less-toggle-1\" class=\"show-more-show-less-toggleable-content\">\n\t\t\t\t\t\n\n\n\n<p>We begin by acknowledging the groundbreaking work in protein structure prediction. However, proteins are dynamic, constantly changing their conformation. This is where our research takes a pioneering step, focusing on the equilibrium distributions of these structures versus a static image.&nbsp;<\/p>\n\n\n\n<p>Understanding equilibrium distributions in molecular science is challenging but exciting because it opens up new possibilities in diverse fields. By learning about the different states and the behavior of molecules, scientists can make breakthroughs in developing new drugs, creating advanced materials, and understanding biological processes.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Our new approach, the Distributional Graphormer, brings generative AI technologies into thermodynamics, offering efficiency and accuracy to obtain the equilibrium distribution for any molecular system, far beyond traditional methods like molecular dynamics simulation. It begins with any descriptor of a molecular system. For example, the sequence of amino acids revolutionized the prediction of molecular systems\u2019 equilibrium distribution.&nbsp;<\/p>\n\n\n\n<p>Let&#8217;s dive into practical implications. Consider the case of B-Raf kinase, a protein linked to cancer. Traditional methods fail to capture its active and inactive states comprehensively. DiG, on the other hand, accurately samples these states, demonstrating its power in understanding the important dynamics.&nbsp;<\/p>\n\n\n\n<p>Let&#8217;s see a real-world application. The ability of DiG to predict a range of conformations of the main proteins of SARS-CoV-2 virus provides insight that could revolutionize how we understand the viral mutations and the development of drugs. DiG can also reveal the interaction between protein and ligands and predict the binding of free energy to aid in modern drug discovery. The transition pathway of conformation can be easily obtained with DiG by a fast interpolation in latent space.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Beyond protein systems, DiG can also predict equilibrium distribution for other molecular systems. For example, this figure shows DiG predicts the density of catalyst-adsorbate systems compared with the results of DFT calculations.<\/p>\n\n\n\n<p>In closing, DiG is a paradigm shift in molecular science\u2014from the structure prediction and the molecular simulation to equilibrium distribution prediction with generative AI. Its potential applications are vast, touching upon areas from bioinformatics to material discovery. I invite you to explore our new findings on the <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2306.05445\" target=\"_blank\" rel=\"noreferrer noopener\">arXiv paper<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> and engage with our <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/distributionalgraphormer.github.io\/\" target=\"_blank\" rel=\"noreferrer noopener\">interactive demo<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> to witness the future of molecular science.<\/p>\n\n\n\n<p>Thank you for your time.<\/p>\n\n\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t<button\n\t\t\t\tclass=\"action-trigger glyph-prepend mt-2 mb-0 show-more-show-less-toggle\"\n\t\t\t\taria-expanded=\"false\"\n\t\t\t\tdata-show-less-text=\"Show less\"\n\t\t\t\ttype=\"button\"\n\t\t\t\taria-controls=\"show-more-show-less-toggle-1\"\n\t\t\t\taria-label=\"Show more content\"\n\t\t\t\tdata-alternate-aria-label=\"Show less content\">\n\t\t\t\tShow more\t\t\t<\/button>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading alignwide\" id=\"related-resources\">Related resources<\/h3>\n\n\n\n<div class=\"wp-block-columns alignwide are-vertically-aligned-top is-layout-flex wp-container-core-columns-is-layout-1 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">Publication<\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-predicting-equilibrium-distributions-for-molecular-systems-with-deep-learning\/\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning\" data-bi-aN=\"citation\" data-bi-cN=\"Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning\">\n\t\t\t\tTowards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning&nbsp;<span class=\"glyph-append glyph-append-chevron-right glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">Blog<\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/distributional-graphormer-toward-equilibrium-distribution-prediction-for-molecular-systems\/\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems\" data-bi-aN=\"citation\" data-bi-cN=\"Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems\">\n\t\t\t\tDistributional Graphormer: Toward equilibrium distribution prediction for molecular systems&nbsp;<span class=\"glyph-append glyph-append-chevron-right glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<li 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