Gabriele Cruciani

发表

Tautomer Enumeration and Stability Prediction for Virtual Screening on Large Chemical Databases

Loriano Storchi, Simon Cross, Francesca Milletti, 2009, J. Chem. Inf. Model..

The Complexity of Molecular Interaction: Molecular Shape Fingerprints by the PathFinder Approach

Gabriele Cruciani, Massimo Baroni, Iain Mcfarlane Mclay, 2006 .

GRIND-derived pharmacophore model for a series of α-tropanyl derivative ligands of the sigma-2 receptor*

Maria Novella Romanelli, Gabriele Cruciani, Paola Gratteri, 2004, J. Comput. Aided Mol. Des..

Molecular Field-Derived Descriptors for the Multivariate Modeling of Pharmacokinetic Data

Wolfgang Guba, Gabriele Cruciani, 2000 .

Eytan Ruppin, Xavier Barril, Gabriele Cruciani, 2017, PLoS Comput. Biol..

A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins

Stefano Alcaro, Andrea Cavalli, Walter Rocchia, 2015, J. Chem. Inf. Model..

Intelligent Combinatorial Libraries

Svante Wold, Michael Sjöström, Svante Wold, 2007 .

Molecular Interaction Fields and 3D-QSAR Studies of p53-MDM2 Inhibitors Suggest Additional Features of Ligand-Target Interaction

Matteo Magnani, Gabriele Cruciani, Andrea Carotti, 2010, J. Chem. Inf. Model..

3D-QSAR methods on the basis of ligand–receptor complexes. Application of COMBINE and GRID/GOLPE methodologies to a series of CYP1A2 ligands

Ferran Sanz, Juan José Lozano, Nuria B. Centeno, 2000, J. Comput. Aided Mol. Des..

GRID-Based Three-Dimensional Pharmacophores I: FLAPpharm, a Novel Approach for Pharmacophore Elucidation

Simon Cross, Gabriele Cruciani, Massimo Baroni, 2012, J. Chem. Inf. Model..

QSAR study and VolSurf characterization of anti-HIV quinolone library

Gabriele Cruciani, Oriana Tabarrini, Violetta Cecchetti, 2001, J. Comput. Aided Mol. Des..

Sequence Protein Identification by Randomized Sequence Database and Transcriptome Mass Spectrometry (SPIDER-TMS): From Manual to Automatic Application of a ‘de Novo Sequencing’ Approach

Donatello Santoro, Giansalvatore Mecca, Gabriele Cruciani, 2016, European journal of mass spectrometry.

Handling Information from 3D Grid Maps for QSAR Studies

Gabriele Cruciani, Manuel Pastor, Sergio Clementi, 2000 .

DESIGNING NOVEL INHIBITORS FOR CARBAPENEMASES: A MULTIDISCIPLINARY APPROACH

Francesca Spyrakis, Maria Paola Costi, Gabriele Cruciani, 2016 .

Selection of informative structures for QSAR studies

Gabriele Cruciani, Massimo Baroni, Bert Skagerberg, 1991 .

Synthetic Peptide Libraries. From random mixtures to in vivo testing.

Gabriele Cruciani, Menotti Ruvo, Annamaria Sandomenico, 2018, Current medicinal chemistry.

GRID-Based Three-Dimensional Pharmacophores II: PharmBench, a Benchmark Data Set for Evaluating Pharmacophore Elucidation Methods

Stefano Alcaro, Federica Moraca, Simona Distinto, 2012, J. Chem. Inf. Model..

Quantitative structure–property relationship modeling of ruthenium sensitizers for solar cells applications: novel tools for designing promising candidates

Gabriele Cruciani, Sara Tortorella, Gabriele Marotta, 2015 .

Grid-derived structure-based 3D pharmacophores and their performance compared to docking.

Gabriele Cruciani, Simon S. Cross, S. Cross, 2010, Drug discovery today. Technologies.

Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow.

Francesca Spyrakis, Paolo Benedetti, Gabriele Cruciani, 2018, ACS medicinal chemistry letters.

Modeling Phospholipidosis Induction: Reliability and Warnings

Gabriele Cruciani, Daniela Bonelli, Laura Goracci, 2013, J. Chem. Inf. Model..

A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application

Gabriele Cruciani, Jonathan S. Mason, Francesca Perruccio, 2007, J. Chem. Inf. Model..

Subspace discriminant index to expedite exploration of multi-class omics data

José Camacho, Gabriele Cruciani, Tullia Gallina Toschi, 2020 .

High-Throughput Virtual Screening of Proteins Using GRID Molecular Interaction Fields

James E. J. Mills, Gabriele Cruciani, Jonathan S. Mason, 2010, J. Chem. Inf. Model..

New and Original pKa Prediction Method Using Grid Molecular Interaction Fields

Loriano Storchi, Francesca Milletti, Gianluca Sforna, 2007, J. Chem. Inf. Model..

Targeting the Conformational Transitions of MDM2 and MDMX: Insights into Dissimilarities and Similarities of p53 Recognition

Gabriele Cruciani, Andrea Carotti, Antonio Macchiarulo, 2008, J. Chem. Inf. Model..

Mining large chemical spaces in lead and drug discovery

Tudor I. Oprea, Cristian G. Bologa, Bruce S. Edwards, 2006 .

Computational solutions in redox lipidomics - Current strategies and future perspectives.

Gabriele Cruciani, Zhixu Ni, Maria Fedorova, 2019, Free radical biology & medicine.

RECENT DEVELOPMENTS IN 3D-QSAR METHODOLOGIES

Gabriele Cruciani, Massimo Baroni, Manuel Pastor, 1998 .

Chemometric strategy in drug design

Gabriele Cruciani, M. L. Tosato, Bert Skagerberg, 1989 .

ADME-Space: a new tool for medicinal chemists to explore ADME properties

Alban Arrault, Philippe Vayer, Gabriele Cruciani, 2017, Scientific Reports.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications

Loriano Storchi, Gabriele Cruciani, Giovanni Bocci, 2021, J. Comput. Chem..

Small molecule inhibitors of influenza A and B viruses that act by disrupting subunit interactions of the viral polymerase

Giorgio Palù, Gabriele Cruciani, Laura Goracci, 2012, Proceedings of the National Academy of Sciences.

BioGPS: Navigating biological space to predict polypharmacology, off‐targeting, and selectivity

Gabriele Cruciani, Massimo Baroni, Lydia Siragusa, 2015, Proteins.

Surface descriptors for protein-ligand affinity prediction.

Gabriele Cruciani, Ismael Zamora, Tudor Oprea, 2003, Journal of medicinal chemistry.

Improving the prediction of the brain disposition for orally administered drugs using BDDCS.

Tudor I. Oprea, Leslie Z Benet, Gabriele Cruciani, 2012, Advanced drug delivery reviews.

Molecular interaction fields in drug discovery: recent advances and future perspectives

Stefano Alcaro, Anna Artese, Lucia Parrotta, 2013 .

Targeting Cystalysin, a Virulence Factor of Treponema denticola‐Supported Periodontitis

Pietro Cozzini, Glen E Kellogg, Francesca Spyrakis, 2014, ChemMedChem.

Disjoint Principal Properties of Organic Substituents

Gabriele Cruciani, Sergio Clementi, H. van de Waterbeemd, 1995 .

3D QSAR The Integration of QSAR with Molecular Modeling

Roberto Todeschini, Gabriele Cruciani, Laura Bonati, 1994 .

Experimental Design in Synthesis Planning and Structure‐Property Correlations

Gabriele Cruciani, Lennart Eriksson, Sergio Clementi, 1995 .

Screening and confirmatory method for multiclass determination of 62 antibiotics in meat.

Simone Moretti, Roberta Galarini, Gabriele Cruciani, 2016, Journal of chromatography. A.

Exposition and reactivity optimization to predict sites of metabolism in chemicals.

Paolo Benedetti, Gabriele Cruciani, Massimo Baroni, 2013, Drug discovery today. Technologies.

Suitability of molecular descriptors for database mining. A comparative analysis.

Gabriele Cruciani, Raimund Mannhold, Manuel Pastor, 2002, Journal of medicinal chemistry.

Tautomer Enumeration and Stability Prediction for Virtual Screening on Large Chemical Databases.

Loriano Storchi, Gabriele Cruciani, Simon Cross, 2009 .

A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR.

Gabriele Cruciani, Meng Zhang, Roy J Vaz, 2005, Journal of medicinal chemistry.

A novel lipidomics-based approach to evaluating the risk of clinical hepatotoxicity potential of drugs in 3D human microtissues.

Wolfgang Moritz, Gabriele Cruciani, Jan Lichtenberg, 2019, Chemical research in toxicology.

Some applications of the partial least-squares method

Gabriele Cruciani, Sergio Clementi, G. Cruciani, 1986 .

LC/MS lipid profiling from human serum: a new method for global lipid extraction

Gabriele Cruciani, Laura Goracci, L. Goracci, 2014, Analytical and Bioanalytical Chemistry.

Principal Properties for Aromatic Substituents. A Multivariate Approach for Design in QSAR

Gabriele Cruciani, B. Skagerberg, Sergio Clementi, 1989 .

Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case

Francesca Spyrakis, Maria Paola Costi, Gabriele Cruciani, 2016, ChemMedChem.

BDDCS class prediction for new molecular entities.

Tudor I. Oprea, Leslie Z Benet, Gabriele Cruciani, 2012, Molecular pharmaceutics.

Ligand-based virtual screening and ADME-tox guided approach to identify triazolo-quinoxalines as folate cycle inhibitors.

Maria Paola Costi, Giambattista Guaitoli, Gabriele Cruciani, 2010, Bioorganic & medicinal chemistry.

Transporter‐Mediated Efflux Influences CNS Side Effects: ABCB1, from Antitarget to Target

Tudor I. Oprea, Gabriele Cruciani, Emanuele Carosati, 2010, Molecular informatics.

Molecular fields in drug discovery: getting old or reaching maturity?

Gabriele Cruciani, Simon Cross, S. Cross, 2010, Drug discovery today.

Conformer- and alignment-independent model for predicting structurally diverse competitive CYP2C9 inhibitors.

Anders Karlén, Gabriele Cruciani, Massimo Baroni, 2004, Journal of medicinal chemistry.

Application of chemometrics to the screening of hazardous chemicals A case study

Gabriele Cruciani, M. L. Tosato, Claudio Chiorboli, 1992 .

Some considerations on the predictions of pharmacokinetic alterations in subjects with liver disease

Gabriele Cruciani, Italo Poggesi, Laura Goracci, 2014, Expert opinion on drug metabolism & toxicology.

Disrupting Protein–Protein Interfaces Using GRID Molecular Interaction Fields

Stefano Alcaro, Gabriele Cruciani, Massimo Baroni, 2013 .

GRID‐Derived Molecular Interaction Fields for Predicting the Site of Metabolism in Human Cytochromes

Gabriele Cruciani, Massimo Baroni, Yasmin Aristei, 2006 .

D-Optimal Designs in QSAR

Svante Wold, Gabriele Cruciani, Massimo Baroni, 1993 .

Predicting drug metabolism: a site of metabolism prediction tool applied to the cytochrome P450 2C9.

Gabriele Cruciani, Ismael Zamora, Lovisa Afzelius, 2003, Journal of medicinal chemistry.

Isozyme-Specific Ligands for O-acetylserine sulfhydrylase, a Novel Antibiotic Target

Pietro Cozzini, Francesca Spyrakis, Paolo Benedetti, 2013, PloS one.

On the Mechanism of Action of Anti-Inflammatory Activity of Hypericin: An In Silico Study Pointing to the Relevance of Janus Kinases Inhibition

Gabriele Cruciani, Luca Dellafiora, Gianni Galaverna, 2018, Molecules.

In silico pKa Prediction and ADME Profiling

Loriano Storchi, Gabriele Cruciani, Laura Goracci, 2010 .

Extending pKa prediction accuracy: high-throughput pKa measurements to understand pKa modulation of new chemical series.

Manfred Kansy, Loriano Storchi, Gabriele Cruciani, 2010, European journal of medicinal chemistry.

Predicting protein pKa by environment similarity

Loriano Storchi, Gabriele Cruciani, G. Cruciani, 2009, Proteins.

Three-Dimensional Quantitative Structure-Properties Relationships

Gabriele Cruciani, Emanuele Carosati, Sergio Clementi, 2003 .

Molecular interaction fields : applications in drug discovery and ADME prediction

Gabriele Cruciani, G. Cruciani, 2005 .

Structural differences of matrix metalloproteinases with potential implications for inhibitor selectivity examined by the GRID/CPCA approach.

Gabriele Cruciani, Flemming Steen Jørgensen, Inge Thøger Christensen, 2002, Journal of medicinal chemistry.

Pharmacophore, drug metabolism, and pharmacokinetics models on non-peptide AT1, AT2, and AT1/AT2 angiotensin II receptor antagonists.

Gabriele Cruciani, Raimund Mannhold, Giuliano Berellini, 2005, Journal of medicinal chemistry.

A New Set of Principal Properties for Heteroaromatics Obtained by GRID

Gabriele Cruciani, Giuseppe Musumarra, Daniela Riganelli, 1996 .

Theoretical versus empirical molecular descriptors in monosubstituted benzenes: A chemometric study

Marina Cocchi, Gabriele Cruciani, Maria Cristina Menziani, 1992 .

New and Original pKa Prediction Method Using Grid Molecular Interaction Fields.

Loriano Storchi, Gabriele Cruciani, G. Cruciani, 2008 .

In silico pKa Prediction and ADME Profiling

Loriano Storchi, Gabriele Cruciani, Laura Goracci, 2009, Chemistry & biodiversity.

Discovery of novel inhibitors of the NorA multidrug transporter of Staphylococcus aureus.

Stefano Sabatini, Gabriele Cruciani, Emanuele Carosati, 2011, Journal of medicinal chemistry.

Peptide studies by means of principal properties of amino acids derived from MIF descriptors

Gabriele Cruciani, Massimo Baroni, Emanuele Carosati, 2004 .

GOLPE-Guided Region Selection

Gabriele Cruciani, Sergio Clementi, Manuel Pastor, 1998 .

Molecular Design

Jürgen Bajorath, Uli Fechner, Hans-Joachim Böhm, 2007 .

A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Liands and Proteins (FLAP): Theory and Application.

Gabriele Cruciani, Jonathan S. Mason, Francesca Perruccio, 2007 .

Predicting human serum albumin affinity of interleukin-8 (CXCL8) inhibitors by 3D-QSPR approach.

Gabriele Cruciani, Alessio Moriconi, G. Cruciani, 2005, Journal of medicinal chemistry.

GRID/GOLPE 3D-QSAR Study on a Set of Benzamides and Naphtamides, with Affinity for the Dopamine D3 Receptor Subtype

Age K. Smilde, Gabriele Cruciani, Sergio Clementi, 1997 .

GOLPE: An advanced chemometric tool for 3D QSAR problems

Gabriele Cruciani, Massimo Baroni, Daniela Riganelli, 1993 .

Predictive models for hERG potassium channel blockers.

Gabriele Cruciani, Roy J Vaz, Arnaldo Fravolini, 2005, Bioorganic & medicinal chemistry letters.