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      High-Throughput Docking Using Quantum Mechanical Scoring

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          Abstract

          Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

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          Most cited references60

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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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            End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

            Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein-ligand binding, protein-protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.
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              Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters

              Modern semiempirical methods are of sufficient accuracy when used in the modeling of molecules of the same type as used as reference data in the parameterization. Outside that subset, however, there is an abundance of evidence that these methods are of very limited utility. In an attempt to expand the range of applicability, a new method called PM7 has been developed. PM7 was parameterized using experimental and high-level ab initio reference data, augmented by a new type of reference data intended to better define the structure of parameter space. The resulting method was tested by modeling crystal structures and heats of formation of solids. Two changes were made to the set of approximations: a modification was made to improve the description of noncovalent interactions, and two minor errors in the NDDO formalism were rectified. Average unsigned errors (AUEs) in geometry and ΔH f for PM7 were reduced relative to PM6; for simple gas-phase organic systems, the AUE in bond lengths decreased by about 5 % and the AUE in ΔH f decreased by about 10 %; for organic solids, the AUE in ΔH f dropped by 60 % and the reduction was 33.3 % for geometries. A two-step process (PM7-TS) for calculating the heights of activation barriers has been developed. Using PM7-TS, the AUE in the barrier heights for simple organic reactions was decreased from values of 12.6 kcal/mol-1 in PM6 and 10.8 kcal/mol-1 in PM7 to 3.8 kcal/mol-1. The origins of the errors in NDDO methods have been examined, and were found to be attributable to inadequate and inaccurate reference data. This conclusion provides insight into how these methods can be improved. Electronic supplementary material The online version of this article (doi:10.1007/s00894-012-1667-x) contains supplementary material, which is available to authorized users.
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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
                21 April 2020
                2020
                : 8
                : 246
                Affiliations
                [1] 1Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral , Pilar, Argentina
                [2] 2Facultad de Ciencias Biomédicas and Facultad de Ingeniería, Universidad Austral , Pilar, Argentina
                [3] 3Austral Institute for Applied Artificial Intelligence, Universidad Austral , Pilar, Argentina
                Author notes

                Edited by: Simone Brogi, University of Pisa, Italy

                Reviewed by: Tingjun Hou, Zhejiang University, China; Stefano Moro, University of Padova, Italy; Vladimir Borisovich Sulimov, Lomonosov Moscow State University, Russia

                This article was submitted to Theoretical and Computational Chemistry, a section of the journal Frontiers in Chemistry

                Article
                10.3389/fchem.2020.00246
                7186494
                0dc9d95f-c618-458c-97ef-4d48de6ccd9d
                Copyright © 2020 Cavasotto 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(s) 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
                : 02 February 2020
                : 16 March 2020
                Page count
                Figures: 2, Tables: 4, Equations: 6, References: 73, Pages: 10, Words: 7842
                Funding
                Funded by: Agencia Nacional de Promoción Científica y Tecnológica 10.13039/501100003074
                Categories
                Chemistry
                Original Research

                high-throughput docking,structure-based drug design,molecular docking,quantum mechanics,semi-empirical methods

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