scholarly journals Selective Tight Binding Inhibitors of Trypanosomal Glyceraldehyde-3-phosphate Dehydrogenase via Structure-Based Drug Design

1998 ◽  
Vol 41 (24) ◽  
pp. 4790-4799 ◽  
Author(s):  
Alex M. Aronov ◽  
Christophe L. M. J. Verlinde ◽  
Wim G. J. Hol ◽  
Michael H. Gelb
Keyword(s):  
Drug Design ◽  
Tight Binding ◽  
Structure Based Drug Design ◽  
Binding Inhibitors

ChemInform Abstract: Selective Tight Binding Inhibitors of Trypanosomal Glyceraldehyde-3-phosphate Dehydrogenase via Structure-Based Drug Design.

ChemInform ◽  
2010 ◽  
Vol 30 (13) ◽  
pp. no-no
Author(s):  
Alex M. Aronov ◽  
Christophe L. M. J. Verlinde ◽  
Wim G. J. Hol ◽  
Michael H. Gelb
Keyword(s):  
Drug Design ◽  
Tight Binding ◽  
Structure Based Drug Design ◽  
Binding Inhibitors

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.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

A Perspective on Water Site Prediction Methods for Structure Based Drug Design

2017 ◽  
Vol 17 (23) ◽  
Author(s):  
Alan P. Graves ◽  
Ian D. Wall ◽  
Colin M. Edge ◽  
James M. Woolven ◽  
Guanglei Cui ◽  
...  
Keyword(s):  
Drug Design ◽  
Prediction Methods ◽  
Structure Based Drug Design ◽  
Site Prediction

Computer-Aided Structure Based Drug Design Approaches for the Discovery of New Anti-CHIKV Agents

2017 ◽  
Vol 13 (4) ◽  
Author(s):  
Surender Singh Jadav ◽  
Barij Nayan Sinha ◽  
Rolf Hilgenfeld ◽  
Venkatesan Jayaprakash
Keyword(s):  
Drug Design ◽  
Structure Based Drug Design ◽  
Computer Aided
Sign in / Sign up

Export Citation Format

Share Document