<\/p>\n\n\n\n
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><\/p>\n\n\n\n Multitarget-directed Ligands (MTDLs)<\/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><\/p>\n\n\n\n Molecular properties prediction with Deep-Learning algorithm.<\/strong><\/p>\n\n\n\n Our team has recently applied deep-learning methods to molecular-property prediction. A major focus is environmental toxicity, particularly the prediction and prioritisation of the Persistence, Bioaccumulation and Toxicity (PBT) profiles of pharmaceuticals (contaminants of emerging concern) and other biologically active compounds. These approaches can help assess the environmental fate of pharmaceuticals and their occurrence in environmental and agro-food matrices. Such computational tools can support monitoring and prevention strategies, quality certification and technical or regulatory decision-making.<\/p><\/p>\n\n\n\n Ligand binding kinetics and free-energy calculations<\/strong><\/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.<\/p><\/p>\n\n\n\n Development of a platform for efficient preclinical drug discovery<\/strong><\/p>\n\n\n\n We contribute to developing and maintaining an integrated platform that combines a HPC infrastructure with biophysical assays (SPR and MST) 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. Ligand Binding and Allosteric Signalling at the Protein-Membrane Interface<\/strong><\/p>\n\n\n\n We are interested in developing and applying computational methods to predict ligand interactions and investigate potential allosteric signalling at protein\u2013membrane interfaces. The methodological framework integrates advanced flexible docking, co-folding algorithms, atomistic and coarse-grained molecular dynamics simulations, interaction-network analysis and free-energy calculations. G-protein-coupled receptors (GPCRs) represent one important protein family to which these approaches will be applied.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":" 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. Multitarget-directed Ligands (MTDLs) Our lab is involved […]<\/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":23,"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/pages\/26\/revisions"}],"predecessor-version":[{"id":390,"href":"https:\/\/camd.uniurb.it\/index.php?rest_route=\/wp\/v2\/pages\/26\/revisions\/390"}],"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}]}}
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><\/p><\/p>\n\n\n\n