3D-QSAR methods on the basis of ligand-receptor complexes. Application of COMBINE and GRID/GOLPE methodologies to a series of CYP1A2 ligands. - Juan José Lozano; Manuel Pastor; Gabriele Cruciani; Katrin Gaedt; Nuria B. Centeno; Federico Gago; Ferran Sanz (J. Comput. Aided Mol. Des., 2000)

QSAR study and VolSurf characterization of anti-HIV quinolone library. - Enrica Filipponi; Gabriele Cruciani; Oriana Tabarrini; Violetta Cecchetti; Arnaldo Fravolini (J. Comput. Aided Mol. Des., 2001)

GRIND-derived pharmacophore model for a series of α-tropanyl derivative ligands of the sigma-2 receptor*. - Paola Cratteri; Maria Novella Romanelli; Gabriele Cruciani; Claudia Bonaccini; Fabrizio Melani (J. Comput. Aided Mol. Des., 2004)

A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application. - Massimo Baroni; Gabriele Cruciani; Simone Sciabola; Francesca Perruccio; Jonathan S. Mason (J. Chem. Inf. Model., 2007)

New and Original pKa Prediction Method Using Grid Molecular Interaction Fields. - Francesca Milletti; Loriano Storchi; Gianluca Sforna; Gabriele Cruciani (J. Chem. Inf. Model., 2007)

Targeting the Conformational Transitions of MDM2 and MDMX: Insights into Dissimilarities and Similarities of p53 Recognition. - Antonio Macchiarulo; Nicola Giacche; Andrea Carotti; Massimo Baroni; Gabriele Cruciani; Roberto Pellicciari (J. Chem. Inf. Model., 2008)

Tautomer Enumeration and Stability Prediction for Virtual Screening on Large Chemical Databases. - Francesca Milletti; Loriano Storchi; Gianluca Sforna; Simon Cross; Gabriele Cruciani (J. Chem. Inf. Model., 2009)

Molecular Interaction Fields and 3D-QSAR Studies of p53-MDM2 Inhibitors Suggest Additional Features of Ligand-Target Interaction. - Cristina Dezi; Andrea Carotti; Matteo Magnani; Massimo Baroni; Alessandro Padova; Gabriele Cruciani; Antonio Macchiarulo; Roberto Pellicciari (J. Chem. Inf. Model., 2010)

High-Throughput Virtual Screening of Proteins Using GRID Molecular Interaction Fields. - Simone Sciabola; Robert V. Stanton; James E. J. Mills; Maria M. Flocco; Massimo Baroni; Gabriele Cruciani; Francesca Perruccio; Jonathan S. Mason (J. Chem. Inf. Model., 2010)

GRID-Based Three-Dimensional Pharmacophores I: FLAPpharm, a Novel Approach for Pharmacophore Elucidation. - Simon Cross; Massimo Baroni; Laura Goracci; Gabriele Cruciani (J. Chem. Inf. Model., 2012)

GRID-Based Three-Dimensional Pharmacophores II: PharmBench, a Benchmark Data Set for Evaluating Pharmacophore Elucidation Methods. - Simon Cross; Francesco Ortuso; Massimo Baroni; Giosuè Costa; Simona Distinto; Federica Moraca; Stefano Alcaro; Gabriele Cruciani (J. Chem. Inf. Model., 2012)

Modeling Phospholipidosis Induction: Reliability and Warnings. - Laura Goracci; Martina Ceccarelli; Daniela Bonelli; Gabriele Cruciani (J. Chem. Inf. Model., 2013)

A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins. - Francesca Spyrakis; Paolo Benedetti; Sergio Decherchi; Walter Rocchia; Andrea Cavalli; Stefano Alcaro; Francesco Ortuso; Massimo Baroni; Gabriele Cruciani (J. Chem. Inf. Model., 2015)

Detecting similar binding pockets to enable systems polypharmacology. - Miquel Duran-Frigola; Lydia Siragusa; Eytan Ruppin; Xavier Barril; Gabriele Cruciani; Patrick Aloy (PLoS Comput. Biol., 2017)

How computational chemistry develops: a tribute to Peter Goodford. - Gabriele Cruciani; Yvonne C. Martin; Andy Vinter; Richard Lewis; Federico Gago; Terry R. Stouch (J. Comput. Aided Mol. Des., 2019)

Cover Image. - Sara Tortorella; Emanuele Carosati; Giulia Sorbi; Giovanni Bocci; Simon Cross; Gabriele Cruciani; Loriano Storchi (J. Comput. Chem., 2021)

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications. - Sara Tortorella; Emanuele Carosati; Giulia Sorbi; Giovanni Bocci; Simon Cross; Gabriele Cruciani; Loriano Storchi (J. Comput. Chem., 2021)

FragExplorer: GRID-Based Fragment Growing and Replacement. - Simon Cross; Gabriele Cruciani (J. Chem. Inf. Model., 2022)

CROMATIC: Cross-Relationship Map of Cavities from Coronaviruses. - Lydia Siragusa; Gabriele Menna; Fabrizio Buratta; Massimo Baroni; Jenny Desantis; Gabriele Cruciani; Laura Goracci (J. Chem. Inf. Model., 2022)

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions. - Daniela Trisciuzzi; Lydia Siragusa; Massimo Baroni; Ida Autiero; Orazio Nicolotti; Gabriele Cruciani (J. Chem. Inf. Model., 2022)

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening. - Daniela Trisciuzzi; Lydia Siragusa; Massimo Baroni; Gabriele Cruciani; Orazio Nicolotti (J. Chem. Inf. Model., 2022)

DeepGRID: Deep Learning Using GRID Descriptors for BBB Prediction. - Loriano Storchi; Gabriele Cruciani; Simon Cross (J. Chem. Inf. Model., 2023)