Pairwise GB/SA Scoring Function for Structure-based Drug Design

2004 ◽  
Vol 108 (17) ◽  
pp. 5453-5462 ◽  
Author(s):  
Hao-Yang Liu ◽  
Irwin D. Kuntz ◽  
Xiaoqin Zou
Keyword(s):  
Drug Design ◽  
Scoring Function ◽  
Structure Based Drug Design

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

scholarly journals MOLECULAR DOCKING: AN EXPLANATORY APPROACH IN STRUCTURE-BASED DRUG DESIGNING AND DISCOVERY

2021 ◽  
pp. 6-12
Author(s):  
ADITI SHARMA ◽  
SALONI KUNWAR ◽  
VAISHALI ◽  
VAISHALI AGARWAL ◽  
CHHAYA SINGH ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
De Novo ◽  
Scoring Function ◽  
Binding Mode ◽  
De Novo Drug Design ◽  
Structure Based Drug Design ◽  
Binding Characteristics ◽  
Stable Product ◽  
Novel Concept

Molecular docking is a modeling tool of Bioinformatics which includes two or more molecules which interact to provide a stable product in the form of a complex. Molecular docking is helpful in predicting the 3-d structure of a complex which depends on the binding characteristics of Ligand and target. Also, it is a structure-based virtual screening (SBVS) utilized to keep the 3-d structures of small molecule which are generated by computers into a target structure in various types of conformations, positions and orientations. This molecular docking has come out to be a novel concept with various types of advantages. It behaves as a highly exploring domain due to its significant structure-based drug design (SBDD), Assessment of Biochemical pathways, Lead Optimization and in De Novo drug design. In spite of all potential approaches, there are certain challenges which are-scoring function (differentiate the true binding mode), ligand chemistry (tautomerism and ionization) and receptor flexibility (single conformation of rigid receptor). The area of computer-aided drug design and discovery (CADDD) has achieved large favorable outcomes in the past few years. CADD has been adopted by various big pharmaceutical companies for leading discoveries of drugs. Many researchers have worked in order to examine different docking algorithms and to predict molecules' active site. Hence, this Review article depicts the whole sole of Molecular Docking.

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

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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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