2021
Methods in Molecular Biology book series (MIMB, volume 2266)
Protein-Ligand Interactions and Drug Design Book
Humana, New York, NY, 2021.
Abstract | Links | BibTeX | Tags: Book, Chemoinformatics, Molecular Modeling, Protocol
@book{inbookseries(MIMB2021,
title = {Protein-Ligand Interactions and Drug Design},
author = {Methods in Molecular Biology book series (MIMB, volume 2266)},
editor = {Flavio Ballante},
url = {https://link.springer.com/book/10.1007/978-1-0716-1209-5},
doi = {doi.org/10.1007/978-1-0716-1209-5},
year = {2021},
date = {2021-03-24},
publisher = {Humana, New York, NY},
abstract = {This detailed book collects modern and established computer-based methods aimed at addressing the drug discovery challenge from disparate perspectives by exploiting information on ligand-protein recognition. Beginning with methods that allow for the exploration of specific areas of chemical space and the designing of virtual libraries, the volume continues with sections on methods based on docking, quantitative models, and molecular dynamics simulations, which are employed for ligand discovery or development, as well as methods exploiting an ensemble of protein structures for the identification of potential protein targets. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
Authoritative and cutting-edge, Protein-Ligand Interactions and Drug Design provides detailed practical procedures of solid computer-aided drug design methodologies employed to rationalize and optimize protein-ligand interactions, for experienced researchers and novices alike.},
keywords = {Book, Chemoinformatics, Molecular Modeling, Protocol},
pubstate = {published},
tppubtype = {book}
}
Authoritative and cutting-edge, Protein-Ligand Interactions and Drug Design provides detailed practical procedures of solid computer-aided drug design methodologies employed to rationalize and optimize protein-ligand interactions, for experienced researchers and novices alike.
2015
Flavio, Ballante; Marshall, Garland R.
An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design Journal Article
In: Journal of Chemical Information and Modeling, 56 (1), pp. 54-72, 2015.
Abstract | Links | BibTeX | Tags: Chemoinformatics, Clusterizer, DockAccessor, Docking Assessment, Molecular Docking, Molecular Modeling
@article{Ballante2015,
title = {An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design},
author = {Flavio, Ballante and Garland R. Marshall },
editor = {American Chemical Society},
url = {http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00603},
doi = {10.1021/acs.jcim.5b00603},
year = {2015},
date = {2015-12-18},
urldate = {2015-12-18},
journal = {Journal of Chemical Information and Modeling},
volume = {56},
number = {1},
pages = {54-72},
abstract = {Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e. by determining the Docking Accuracy, DA) to select the most appropriate docking procedure, and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different RMSD formulas, scoring functions and format results, it is both difficult and time consuming to: consistently determine and compare their predictive capability to identify the best protocol to be used for the target of interest; extrapolate the binding poses (i.e. Best- docked (BD), Best-cluster (BC) and Best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures, called Clusterizer and DockAccessor have been developed and implemented for use with some common and “free-for-academics” programs, such as: AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC and BF poses, as well as perform consistent cluster and docking accuracy (DA) analyses. Clusterizer and DockAccessor represent two novel tools, (code available over the internet) to collect computationally determined poses as well as detect the most predictive docking approach. Herein, an application to lysine deacetylase (KDAC) inhibitors is illustrated.},
keywords = {Chemoinformatics, Clusterizer, DockAccessor, Docking Assessment, Molecular Docking, Molecular Modeling},
pubstate = {published},
tppubtype = {article}
}
2014
Flavio, Ballante
Application of Medicinal Chemistry Methods on Different Classes of Drugs PhD Thesis
Sapienza University of Rome, 2014.
Abstract | Links | BibTeX | Tags: 3-D QSAutogrid/R, 3D QSAR, Biochemistry, BSAO, Chemoinformatics, Chemometrics, COMBINEr, CoMFA, DABO, DockAccessor, Docking Assessment, HCV NS5B, HDAC, HIV-1, HIV-RT, HSP90, HUVEC, Ligand-Based Design, Molecular Docking, Molecular Modeling, MPGRS, NNRTIs, Opioid-Receptor Antagonists, Organic Chemistry, Pharmacophoric Model, PLS, Tuberculosis, VEGFR-2, Virtual Screening
@phdthesis{Ballante2014b,
title = {Application of Medicinal Chemistry Methods on Different Classes of Drugs},
author = { Ballante Flavio},
editor = {PADIS},
url = {http://hdl.handle.net/11573/918780 },
year = {2014},
date = {2014-01-27},
address = {Department of Chemistry and Tecnology of Drugs},
school = {Sapienza University of Rome},
abstract = {The present doctoral thesis is the result of the work carried out during the three years of my PhD scholarship at the Rome Center for Molecular Design laboratory (RCMD, Department of Chemistry and Drug Technologies, Sapienza University of Rome), under the supervision of Prof. Rino Ragno. The research activity was focused mainly on the design, optimization and application of computational strategies to derive quantitative structure-activity relationships (QSAR, 3-D QSAR, and COMBINE) on different molecular classes of current interest, such as: opioid receptor antagonists (OPAs), Hepatitis C Virus NS5B-Polymerase Inhibitors (NS5B-NNIs), Hystone Deacetylase Inhibitors (HDACIs), Anti- tubercular agents, vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors, HSP90 inhibitors, HIV-1 reverse transcriptase inhibitors (NNRTIs), Bovine Serum Amine Oxidase (BSAO) substrates, etc... Moreover two research periods abroad were performed: the first framed in a LLP Erasmus program collaboration, was conducted for six months at the Laboratoire d'Ingénierie et Moléculaire Pharmacologique Biochimie (LIMBP) of the Université de Lorraine Metz (France), directed by Prof. Gilbert Kirsch, and characterized by the application of organic synthesis to obtain new thienopyrimidinone derivatives as potential inhibitors of vascular endothelial growth factor receptor-2 (VEGFR-2); the second took place, for three months, at the Department of Biochemistry and Molecular Biophysics in Washington University School of Medicine in Saint Louis (MO, USA), under the supervision of Prof. Garland R. Marshall, investigating the activity profile of new Histone Deacetylases (HDACs) inhibitors by the application of the Mobility Shift Assay Technology. Main purpose of this doctoral thesis is to highlight the activities carried out in the different research projects, the applied methodologies and the obtained results. The text starts describing those studies whose results were published in scientific journals (chapters I-VI): the author decided to omit some procedural details, completely reported in the published papers, that would make the text too long, tedious and redundant; therefore readers who want to delve these aspects can also refer to Chapter XII in which is possible to read the original papers; on the contrary for studies that have not yet been published, as those characterizing the Chapters VII and VIII, discussion is adequately detailed. Chapters IX and X report the scientific activities carried out in France and in USA respectively; Chapter XI summarizes all the scientific activities accomplished during the entire PhD course, whereas Chapter XII, as mentioned, contains the published articles.},
keywords = {3-D QSAutogrid/R, 3D QSAR, Biochemistry, BSAO, Chemoinformatics, Chemometrics, COMBINEr, CoMFA, DABO, DockAccessor, Docking Assessment, HCV NS5B, HDAC, HIV-1, HIV-RT, HSP90, HUVEC, Ligand-Based Design, Molecular Docking, Molecular Modeling, MPGRS, NNRTIs, Opioid-Receptor Antagonists, Organic Chemistry, Pharmacophoric Model, PLS, Tuberculosis, VEGFR-2, Virtual Screening},
pubstate = {published},
tppubtype = {phdthesis}
}
2012
Flavio, Ballante; Rino, Ragno
3-D QSAutogrid/R: An Alternative Procedure To Build 3-D QSAR Models. Methodologies and Applications Journal Article
In: Journal of Chemical Information and Modeling, 52 (6), pp. 1674, 2012.
Abstract | Links | BibTeX | Tags: 3-D QSAutogrid/R, 3D QSAR, Chemoinformatics, Chemometrics, CoMFA, HCV NS5B, Molecular Modeling, MPGRS, Opioid-Receptor Antagonists, PLS
@article{Ballante2012,
title = {3-D QSAutogrid/R: An Alternative Procedure To Build 3-D QSAR Models. Methodologies and Applications},
author = { Ballante Flavio and Ragno Rino},
editor = {American Chemical Society},
url = {http://pubs.acs.org/doi/abs/10.1021/ci300123x},
doi = {10.1021/ci300123x},
year = {2012},
date = {2012-06-25},
journal = {Journal of Chemical Information and Modeling},
volume = {52},
number = {6},
pages = {1674},
abstract = {Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server (www.3d-qsar.com) with its code freely available through R-Cran distribution.},
keywords = {3-D QSAutogrid/R, 3D QSAR, Chemoinformatics, Chemometrics, CoMFA, HCV NS5B, Molecular Modeling, MPGRS, Opioid-Receptor Antagonists, PLS},
pubstate = {published},
tppubtype = {article}
}