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      Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

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          Abstract

          Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

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          Most cited references61

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          Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements

          Several modifications that have been made to the NDDO core-core interaction term and to the method of parameter optimization are described. These changes have resulted in a more complete parameter optimization, called PM6, which has, in turn, allowed 70 elements to be parameterized. The average unsigned error (AUE) between calculated and reference heats of formation for 4,492 species was 8.0 kcal mol−1. For the subset of 1,373 compounds involving only the elements H, C, N, O, F, P, S, Cl, and Br, the PM6 AUE was 4.4 kcal mol−1. The equivalent AUE for other methods were: RM1: 5.0, B3LYP 6–31G*: 5.2, PM5: 5.7, PM3: 6.3, HF 6–31G*: 7.4, and AM1: 10.0 kcal mol−1. Several long-standing faults in AM1 and PM3 have been corrected and significant improvements have been made in the prediction of geometries. Figure Calculated structure of the complex ion [Ta6Cl12]2+ (footnote): Reference value in parenthesis Electronic supplementary material The online version of this article (doi:10.1007/s00894-007-0233-4) contains supplementary material, which is available to authorized users.
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            Conductor-like Screening Model for Real Solvents: A New Approach to the Quantitative Calculation of Solvation Phenomena

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              A critical assessment of docking programs and scoring functions.

              Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.
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                Author and article information

                Contributors
                Journal
                Front Chem
                Front Chem
                Front. Chem.
                Frontiers in Chemistry
                Frontiers Media S.A.
                2296-2646
                29 May 2018
                2018
                : 6
                : 188
                Affiliations
                Laboratory of Computational Chemistry and Drug Design, Instituto de Investigación en Biomedicina de Buenos Aires, CONICET, Partner Institute of the Max Planck Society , Buenos Aires, Argentina
                Author notes

                Edited by: Daniela Schuster, Paracelsus Medizinische Privatuniversität, Salzburg, Austria

                Reviewed by: F. Javier Luque, Universitat de Barcelona, Spain; Serdar Durdagi, Bahçeşehir University, Turkey

                This article was submitted to Medicinal and Pharmaceutical Chemistry, a section of the journal Frontiers in Chemistry

                Article
                10.3389/fchem.2018.00188
                5986912
                29896472
                58e5b539-0a9f-4f79-a2a4-c0db1ca2d1f5
                Copyright © 2018 Cavasotto, Adler and Aucar.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 January 2018
                : 09 May 2018
                Page count
                Figures: 0, Tables: 0, Equations: 1, References: 70, Pages: 7, Words: 5634
                Funding
                Funded by: Agencia Nacional de Promoción Científica y Tecnológica 10.13039/501100003074
                Award ID: PICT 2014-3599
                Categories
                Chemistry
                Mini Review

                quantum mechanics,semi-empirical methods,structure-based drug design,molecular docking,drug lead optimization,binding free energy,molecular dynamics

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