{"id":446,"date":"2025-11-24T18:45:52","date_gmt":"2025-11-24T18:45:52","guid":{"rendered":"https:\/\/www.boltzmannmaps.com\/blog\/?p=446"},"modified":"2025-11-24T18:45:53","modified_gmt":"2025-11-24T18:45:53","slug":"pi-pi-interactions-in-bmaps","status":"publish","type":"post","link":"https:\/\/www.boltzmannmaps.com\/blog\/pi-pi-interactions-in-bmaps\/","title":{"rendered":"Introducing real-time quantification of quantum mechanically accurate \u03c0-\u03c0 interactions for drug design in BMaps"},"content":{"rendered":"\n

Today we introduce real-time quantification of \u03c0-\u03c0 interactions in BMaps using quantum mechanically accurate methods. \u03c0-\u03c0 interactions play a crucial part in the stabilizing and selectivity of protein-ligand interactions for drug design. The aromatic amino acids (Phe, Tyr, and Trp) are common interacting species in active sites, and as any examination of recently approved drugs will tell you, aromatic rings are key components of effective drugs for both interactive and structural reasons.<\/p>\n\n\n

\n
\"Nine<\/figure><\/div>\n\n\n

Figure 1. <\/strong>Subset of 2025 FDA approved drugs with aromatic rings<\/p>\n\n\n\n

For example, non-nucleoside reverse transcriptase inhibitors\u00a0are known to interact selectively with HIV-1 reverse transcriptase, but not HIV-2 reverse transcriptase. As studies have shown, the HIV-1 Tyr181\/Tyr188\u00a0create an aromatic cage while HIV-2 lacks this same environment. Mutation studies in HIV-1 have shown that the loss of this aromatic cage knocks out this selective binding [1<\/a>, 2<\/a>, 3<\/a>]. Furthermore, cases like a study of Imatinib with and without inclusion of quantum mechanical (QM) energy terms in the description of protein-ligand interactions have shown 10\u2019s of kcal\/mol inaccuracy versus experimental binding energetics. When the QM energy terms are included, this error goes away. Such results make all but impossible to determine\u00a0rational design trends. [4<\/a>] It is for this reason that accurate quantitative understanding of the pi-pi interactions created by these aromatic rings at the quantum mechanical level is so necessary for designing new small molecules drugs.<\/p>\n\n\n\n

Current Standard of Practice \u2013 Computational Scaling Prevents Quantification for Large Systems Like Proteins With QM\u2026Or At All<\/strong><\/h2>\n\n\n\n

Despite this importance, current methodologies of quantifying \u03c0-\u03c0 interactions take days to weeks for a single calculation, often requiring simplification of the model system to perform the calculation. Therefore, until now, quantifying \u03c0-\u03c0 interactions in drug design campaigns has been impractical.  Today, Conifer Point\u2019s BMaps is changing that with the inclusion of atomistically separable quantum mechanical free energy prediction of \u03c0-\u03c0 interactions. Our internally developed machine learning models are trained with density functional theory with long-range dispersion corrected functionals and complete electron wavefunction basis sets. With these quality models we can construct atomistic pictures of protein ligand interactions across the entire protein in seconds<\/strong><\/u>. This capability is currently not available in any other computational chemistry software or machine learning models in the literature. The key reason for this uniqueness lies in the atomistic separability of our models which will be discussed in an upcoming manuscript.<\/p>\n\n\n\n

\"\u03c0-\u03c0<\/figure>\n\n\n\n

Figure 2. <\/strong>Visualization of \u03c0-\u03c0 and Hydrogen Bonds Between 4BZR and K26 in BMaps.<\/p>\n\n\n\n

<\/p>\n\n\n\n

See the Interactions You\u2019ve Been Missing.<\/strong><\/p>\n\n\n\n

Try the \u03c0-\u03c0 Interaction Tool in BMaps today!<\/strong><\/p>\n\n\n\n

Check out our tutorial on how to get your \u03c0-\u03c0 interactions and hydrogen bonding interactions quantified in real time here<\/a> or just ask Gibbs<\/a>!<\/p>\n\n\n\n

Let us know which interaction you want to see<\/strong>
automatically quantified next!<\/strong><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Today we introduce real-time quantification of \u03c0-\u03c0 interactions in BMaps using quantum mechanically accurate methods. \u03c0-\u03c0 interactions play a crucial part in the stabilizing and selectivity of protein-ligand interactions for drug design. The aromatic amino acids (Phe, Tyr, and Trp) are common interacting species in active sites, and as any examination of recently approved drugs … <\/p>\n

Continue reading Introducing real-time quantification of quantum mechanically accurate \u03c0-\u03c0 interactions for drug design in BMaps<\/span> →<\/a><\/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":[],"class_list":["post-446","post","type-post","status-publish","format-standard","hentry","category-product-news"],"yoast_head":"\nIntroducing real-time quantification of quantum mechanically accurate \u03c0-\u03c0 interactions for drug design in BMaps - Boltzmann Maps<\/title>\n<meta name=\"description\" content=\"Discover how BMaps brings real-time, quantum-accurate pi pi interactions in drug design to your browser to reveal hidden binding drivers.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.boltzmannmaps.com\/blog\/pi-pi-interactions-in-bmaps\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introducing real-time quantification of quantum mechanically accurate \u03c0-\u03c0 interactions for drug design in BMaps - 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