![\"\"](\"https:\/\/riteh.uniri.hr\/wp-content\/uploads\/image-92.png\")
<\/figure>\n\n\n\nProject overview illustrating the integration of generative artificial intelligence (AI), molecular dynamics (MD) simulations, and experimental validation, highlighting two complementary strategies, pseudo-labeling and active learning, aimed at improving the likelihood of successful project outcomes.<\/p>\n\n\n\n
<\/p>\n\n\n\n
Project description<\/h2>\n\n\n\n
Can Artificial Intelligence Accelerate the Discovery of New Functional Materials?<\/strong><\/p>\n\n\n\n The SuPer DeShPet 2.0<\/strong> explores this possibility by integrating generative artificial intelligence (AI), molecular dynamics (MD) simulations, and experimental characterization into a unified approach to discovering and understanding supramolecular peptide materials.<\/p>\n\n\n\n Peptides<\/strong> are particularly promising building blocks for biomaterials due to their ability to spontaneously organize into complex supramolecular structures through a process known as self-assembly<\/em>. However, predicting their behaviour remains a major scientific challenge, as even small changes in amino acid sequence or experimental conditions can influence material formation and properties. Additional challenges include the limited integration of computational and experimental research, the lack of standardized datasets, and difficulties in reproducing experimental results.<\/p>\n\n\n\n SuPer DeShPet 2.0 aims to overcome these challenges by integrating three complementary approaches.<\/strong> Generative AI models based on neural networks will be used to propose new peptide sequences with high self-assembly propensity. Molecular dynamics (MD) simulations, with particular emphasis on coarse-grained models, will provide insights into the molecular mechanisms of peptide aggregation and the influence of different conditions on peptide behaviour. The most promising peptide candidates will then be experimentally characterized using appropriate spectroscopic and microscopic techniques to investigate their structural properties and ability to form supramolecular materials.<\/p>\n\n\n\n A key contribution of the project lies in the continuous exchange of information between AI, MD, and experiments<\/strong>. Computational and experimental results will be used to iteratively improve predictive models through active learning, enabling them to account not only for amino acid sequences but also for the conditions under which self-assembly occurs. This iterative approach will facilitate more efficient exploration of the vast peptide sequence space, targeted selection of promising candidates, and a reduced need for extensive experimental screening.<\/p>\n\n\n\n The main aim of the project is to develop an integrated computational\u2013experimental framework for the discovery of new supramolecular peptide materials<\/strong>, as well as other peptide-based systems, enabling a deeper understanding of the relationship between amino acid sequence and material function. Looking ahead, SuPer DeShPet 2.0 will contribute to the development of more reliable approaches for predicting and controlling peptide aggregation, opening new opportunities for more efficient, rational, and sustainable discovery of functional biomaterials.<\/p>\n\n\n\n <\/p>\n\n\n\n 1<\/sup> Faculty of Engineering, University of Rijeka <\/p>\n\n\n\n Funded by Croatian Science Foundation, grant number IP-2025-02-3357.<\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" Acronym: Super DeShPet 2.0 Grant number: IP-2025-02-3357 Duration: 2025 \u2013 2028 Budget: 200.000,00 \u20ac Project overview illustrating the integration of generative artificial intelligence (AI), molecular dynamics (MD) simulations, and experimental validation, highlighting two complementary strategies, pseudo-labeling and active learning, aimed at improving the likelihood of successful project outcomes. Project description Can Artificial Intelligence Accelerate the […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":22495,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-53553","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/pages\/53553","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/comments?post=53553"}],"version-history":[{"count":12,"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/pages\/53553\/revisions"}],"predecessor-version":[{"id":53654,"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/pages\/53553\/revisions\/53654"}],"up":[{"embeddable":true,"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/pages\/22495"}],"wp:attachment":[{"href":"https:\/\/riteh.uniri.hr\/en\/wp-json\/wp\/v2\/media?parent=53553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Research group<\/h2>\n\n\n\n
\n
2<\/sup> Faculty of Biotechnology and Drug Development, University of Rijeka
3<\/sup> Department of chemical sciences, University of Padova, Italy
4<\/sup> Centro de Fisica de Materiales\/Materials Physics Center (CFM\/MPC), San Sebastian, \u0160panjolska
5<\/sup> Faculty of Engineering in Pula, Juraj Dobrila University of Pula
6<\/sup> Jadran-galenski laboratorij (JGL), Rijeka<\/p>\n\n\n\nFunding<\/h2>\n\n\n\n
![\"\"](\"https:\/\/riteh.uniri.hr\/wp-content\/uploads\/hrzz_logo_hr.jpg\"){kind=logo}
<\/figure>\n\n\n\nPublications<\/h2>\n\n\n\n
\n
Conferences<\/h2>\n\n\n\n
\n