GAMESS As a Free Quantum-Mechanical Platform for Drug Research

2013 ◽  
Vol 12 (18) ◽  
pp. 2013-2033
Author(s):  
Yuri Alexeev ◽  
Michael P. Mazanetz ◽  
Osamu Ichihara ◽  
Dmitri G. Fedorov
Keyword(s):  
Drug Design ◽  
Method Development ◽  
Drug Research ◽  
Quantum Mechanical ◽  
Orbital Method ◽  
Structure Based Drug Design ◽  
Sar Studies ◽  
Structure Activity ◽  
Steady Improvement ◽  
Biochemical Systems

Driven by a steady improvement of computational hardware and significant progress in ab initio method development, quantum-mechanical approaches can now be applied to large biochemical systems and drug design. We review the methods implemented in GAMESS, which are suitable to calculate large biochemical systems. An emphasis is put on the fragment molecular orbital method (FMO) and quantum mechanics interfaced with molecular mechanics (QM/MM). The use of FMO in the protein-ligand binding, structure-activity relationship (SAR) studies, fragment- and structure-based drug design (FBDD/SBDD) is discussed in detail.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Dutkiewicz
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Development Project ◽  
Quantum Mechanical ◽  
Prediction Methods ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Quantum Mechanical Description ◽  
Optimization Stage

AbstractDrug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

scholarly journals SQM/COSMO Scoring Function: Reliable Quantum‐Mechanical Tool for Sampling and Ranking in Structure‐Based Drug Design

ChemPlusChem ◽  
2020 ◽  
Vol 85 (11) ◽  
pp. 2361-2361
Author(s):  
Adam Pecina ◽  
Saltuk M. Eyrilmez ◽  
Cemal Köprülüoğlu ◽  
Vijay Madhav Miriyala ◽  
Martin Lepšík ◽  
...  
Keyword(s):  
Drug Design ◽  
Scoring Function ◽  
Quantum Mechanical ◽  
Structure Based Drug Design ◽  
Mechanical Tool

Design of Novel Phosphopantetheine Adenylyltransferase Inhibitors: a Potential New Approach to Tackle Mycobacterium tuberculosis

2021 ◽  
Vol 21 ◽  
Author(s):  
Marina C. Primi ◽  
Maurício T. Tavares ◽  
Larry L. Klein ◽  
Tina Izard ◽  
Carlos M. R. Sant'Anna ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Design ◽  
Rational Design ◽  
Multi Drug Resistance ◽  
Design Strategies ◽  
Structure Based Drug Design ◽  
New Approach ◽  
Docking Simulations ◽  
Structure Activity ◽  
The World

Background: Tuberculosis (TB) has been a challenging disease worldwide, especially for the neglected poor populations. Presently, there are approximately 2 billion people infected with TB worldwide and 10 million people in the world fell ill with active TB, leading to 1.5 million deaths. Introduction: The classic treatment is extensive and the drug and multi drug resistance of Mycobacterium tuberculosis has been a threat to the efficacy of the drugs currently used. Therefore, the rational design of new anti TB candidates is urgently needed. Methods: With the aim of contributing to face this challenge, 78 compounds have been proposed based on SBDD (Structure Based Drug Design) strategies applied to target the M. tuberculosis phosphopantetheine adenylyltransferase (MtPPAT) enzyme. Ligand Based Drug Design (LBDD) strategies were also used for establishing structure activity relationships (SAR) and for optimizing the structures. MtPPAT is important for the biosynthesis of coenzyme A (CoA) and it has been studied recently toward the discovery of new inhibitors. Results : After docking simulations and enthalpy calculations, the interaction of selected compounds with MtPPAT was found to be energetically favorable. The most promising compounds were then synthesized and submitted to anti M. tuberculosis and MtPPAT inhibition assays. Conclusion: One of the compounds synthesized (MCP163), showed the highest activity in both of these assays.

scholarly journals Front Cover: SQM/COSMO Scoring Function: Reliable Quantum‐Mechanical Tool for Sampling and Ranking in Structure‐Based Drug Design (ChemPlusChem 11/2020)

ChemPlusChem ◽  
2020 ◽  
Vol 85 (11) ◽  
pp. 2357-2357
Author(s):  
Adam Pecina ◽  
Saltuk M. Eyrilmez ◽  
Cemal Köprülüoğlu ◽  
Vijay Madhav Miriyala ◽  
Martin Lepšík ◽  
...  
Keyword(s):  
Drug Design ◽  
Scoring Function ◽  
Quantum Mechanical ◽  
Structure Based Drug Design ◽  
Front Cover ◽  
Mechanical Tool

SQM/COSMO Scoring Function: Reliable Quantum‐Mechanical Tool for Sampling and Ranking in Structure‐Based Drug Design

ChemPlusChem ◽  
2020 ◽  
Vol 85 (11) ◽  
pp. 2362-2371
Author(s):  
Adam Pecina ◽  
Saltuk M. Eyrilmez ◽  
Cemal Köprülüoğlu ◽  
Vijay Madhav Miriyala ◽  
Martin Lepšík ◽  
...  
Keyword(s):  
Drug Design ◽  
Scoring Function ◽  
Quantum Mechanical ◽  
Structure Based Drug Design ◽  
Mechanical Tool

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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