[2410.18101] Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective

<!DOCTYPE html> <html lang="en"> <head> <title>[2410.18101] Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2410.18101"/> <meta name="description" content="Abstract page for arXiv paper 2410.18101: Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective" /> <meta property="og:url" content="https://arxiv.org/abs/2410.18101v2" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data has been collected, a quantitative structure-activity relationship (QSAR) can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces, to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research \& development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skin care products."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Molecular Dynamics and Machine Learning Unlock Possibilities in..."/> <meta name="twitter:description" content="Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective" /><meta name="citation_author" content="Xu, Yuzhi" /><meta name="citation_author" content="Ni, Haowei" /><meta name="citation_author" content="Gao, Qinhui" /><meta name="citation_author" content="Chang, Chia-Hua" /><meta name="citation_author" content="Huo, Yanran" /><meta name="citation_author" content="Zhao, Fanyu" /><meta name="citation_author" content="Hu, Shiyu" /><meta name="citation_author" content="Xia, Wei" /><meta name="citation_author" content="Zhang, Yike" /><meta name="citation_author" content="Grovu, Radu" /><meta name="citation_author" content="He, Min" /><meta name="citation_author" content="Zhang, John. Z. H." /><meta name="citation_author" content="Wang, Yuanqing" /><meta name="citation_doi" content="10.1063/5.0245365" /><meta name="citation_date" content="2024/10/08" /><meta name="citation_online_date" content="2024/10/28" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2410.18101" /><meta name="citation_arxiv_id" content="2410.18101" /><meta name="citation_abstract" content="Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data has been collected, a quantitative structure-activity relationship (QSAR) can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces, to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research \& development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skin care products." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" 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Perspective</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/physics?searchtype=author&query=Xu,+Y" rel="nofollow">Yuzhi Xu</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Ni,+H" rel="nofollow">Haowei Ni</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Gao,+Q" rel="nofollow">Qinhui Gao</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Chang,+C" rel="nofollow">Chia-Hua Chang</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Huo,+Y" rel="nofollow">Yanran Huo</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Zhao,+F" rel="nofollow">Fanyu Zhao</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Hu,+S" rel="nofollow">Shiyu Hu</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Xia,+W" rel="nofollow">Wei Xia</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Zhang,+Y" rel="nofollow">Yike Zhang</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Grovu,+R" rel="nofollow">Radu Grovu</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=He,+M" rel="nofollow">Min He</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Zhang,+J+Z+H" rel="nofollow">John. Z. H. Zhang</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Wang,+Y" rel="nofollow">Yuanqing Wang</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective, by Yuzhi Xu and 12 other authors</div> <a class="mobile-submission-download" href="/pdf/2410.18101">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2410.18101v2">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data has been collected, a quantitative structure-activity relationship (QSAR) can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces, to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research \& development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skin care products. </blockquote>  <div class="metatable"> <table summary="Additional metadata"><tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Chemical Physics (physics.chem-ph)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2410.18101">arXiv:2410.18101</a> [physics.chem-ph]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2410.18101v2">arXiv:2410.18101v2</a> [physics.chem-ph]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a 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[2410.18101] Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective