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Computer-assisted molecular design<\/strong><\/p>\n\n\n\n Our research mainly focuses on leveraging computer-assisted molecular modeling to design and discover new therapeutic agents. Our expertise spans protein-ligand interaction modeling, structure- and ligand-based drug design, molecular dynamics with enhanced sampling techniques, QSAR\/QSPR analyses, and the integration of machine learning in drug discovery pipelines.<\/p>\n\n\n\n Polypharmacology<\/strong><\/p>\n\n\n\n Our lab is involved in several drug discovery campaigns focused on identifying multi-target ligands. These efforts target widely researched pharmacological proteins, such as kinases and GPCRs, alongside disease-specific proteins. Our in silico techniques include classical molecular docking, precision-tuned library generation, and AI-driven data analysis. Recent successes include the development of a dual GSK-3 beta\/D3 receptor ligand for bipolar disorder treatment and a multitarget-directed ligand for multiple sclerosis with combined anti-inflammatory and remyelinating properties (MARs).<\/p>\n\n\n\n Our group is proficient in the use of different enhanced sampling techniques, including metadynamics, adiabatic-bias MD, umbrella sampling and accelerated MD, for the prediction of binding free-energy and kinetics of systems of pharmaceutical interest. These approaches are exploited for the study of ligand binding and unbinding events, conformational transitions in protein-ligand systems and to develop post-processing workflows for virtual screening results. We contribute to developing and maintaining an integrated platform that combines a HPC infrastructure with biophysical assays (SPR, MST, and CD) to support both academic and industrial drug discovery efforts, accessible to third-party collaborators. Our platform enables virtual screening of ultra-large libraries using structure- and ligand-based methods, incorporates machine learning for compound prioritization, and supports multi-scale workflows for identifying covalent ligands. Additionally, our screening protocols can employ a proprietary virtual library featuring compounds synthesized at the University of Urbino. GPCRs allostery, including ALLODD \u2013 Allostery in Drug Discovery<\/strong><\/p>\n\n\n\n This research project focuses on developing computational methods to enhance our understanding of allostery in G-protein coupled receptors (GPCRs), with particular emphasis on the protein-membrane interface.<\/p>\n\n\n\n Activities<\/em><\/p>\n\n\n\n Funded by EU Horizon 2020 MSCA Program under grant agreement 956314 (ALLODD)<\/em><\/p>\n<\/p>\n\n\n Molecular properties prediction with Deep-Learning algorithm.<\/strong> Computer-assisted molecular design Our research mainly focuses on leveraging computer-assisted molecular modeling to design and discover new therapeutic agents. Our expertise spans protein-ligand interaction modeling, structure- and ligand-based drug design, molecular dynamics with enhanced sampling techniques, QSAR\/QSPR analyses, and the integration of machine learning in drug discovery pipelines. Polypharmacology Our lab is involved in several […]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","footnotes":""},"class_list":["post-26","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/pages\/26","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=26"}],"version-history":[{"count":18,"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/pages\/26\/revisions"}],"predecessor-version":[{"id":183,"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/pages\/26\/revisions\/183"}],"wp:attachment":[{"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Ligand binding kinetics and free-energy calculations<\/strong><\/p>\n\n\n\n
Development of a platform for efficient preclinical drug discovery<\/strong><\/p>\n\n\n\n
Funded by PNRR \u2013 Missione 4 \u2013 Componente 2 \u2013 Progetto \u201cInnovation, digitalisation and sustainability for the diffused economy in Central Italy\u201d (VITALITY)<\/em>
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Our team has recently used deep learning for predicting molecular properties. We primarily focus on environmental toxicity predicting Persistence, Bioaccumulation, and Toxicity (PBT) profiles of biologically active compounds and docking-based molecular prioritization within ultra-large libraries.
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