scholarly journals Rationally designed selective inhibitors of trypanothione reductase. Phenothiazines and related tricyclics as lead structures

1992 ◽  
Vol 286 (1) ◽  
pp. 9-11 ◽  
Author(s):  
T J Benson ◽  
J H McKie ◽  
J Garforth ◽  
A Borges ◽  
A H Fairlamb ◽  
...  
Keyword(s):  
Drug Design ◽  
Glutathione Reductase ◽  
Binding Site ◽  
Molecular Modelling ◽  
Tricyclic Antidepressants ◽  
Rational Drug Design ◽  
Trypanothione Reductase ◽  
Tricyclic Compounds ◽  
Rational Drug ◽  
Anti Oxidant

Trypanothione reductase, an essential component of the anti-oxidant defences of parasitic trypanosomes and Leishmania, differs markedly from the equivalent host enzyme, glutathione reductase, in the binding site for the disulphide substrate. Molecular modelling of this region suggested that certain tricyclic compounds might bind selectively to trypanothione reductase without inhibiting host glutathione reductase. This was confirmed by testing 30 phenothiazine and tricyclic antidepressants, of which clomipramine was found to be the most potent, with a K(i) of 6 microM, competitive with respect to trypanothione. Many of these compounds have been noted previously to have anti-trypanosomal and anti-leishmanial activity and thus they can serve as lead structures for rational drug design.

scholarly journals Structural insights into targeting of the colchicine binding site by ELR510444 and parbendazole to achieve rational drug design

RSC Advances ◽  
2021 ◽  
Vol 11 (31) ◽  
pp. 18938-18944
Author(s):  
Jia-Hong Lei ◽  
Ling-Ling Ma ◽  
Jing-Hong Xian ◽  
Hai Chen ◽  
Jian-Jian Zhou ◽  
...  
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Crystal Structures ◽  
Rational Drug Design ◽  
Colchicine Binding Site ◽  
Rational Drug ◽  
Structural Insights

Crystal structures of tubulin complexed with ELR510444 and parbendazole facilitate the design of novel colchicine binding site inhibitors.

scholarly journals Structure-based identification of a potential non-catalytic binding site for rational drug design in the fructose 1,6-biphosphate aldolase from Giardia lamblia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sara-Teresa Méndez ◽  
Adriana Castillo-Villanueva ◽  
Karina Martínez-Mayorga ◽  
Horacio Reyes-Vivas ◽  
Jesús Oria-Hernández
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Giardia Lamblia ◽  
Rational Drug Design ◽  
Rational Drug

Rational drug design of 6-substituted 4-anilino-2-phenylpyrimidines for exploration of novel ABCG2 binding site

2020 ◽  
pp. 113045
Author(s):  
Katja Silbermann ◽  
Jiyang Li ◽  
Vigneshwaran Namasivayam ◽  
Sven Marcel Stefan ◽  
Michael Wiese
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Rational Drug Design ◽  
Rational Drug

Antipicornavirus Compounds: Use of Rational Drug Design and Molecular Modelling

1993 ◽  
Vol 4 (1) ◽  
pp. 1-10 ◽  
Author(s):  
G. D. Diana ◽  
T. J. Nitz ◽  
J. P. Mallamo ◽  
A. Treasurywala
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Rational Drug Design ◽  
Human Rhinovirus ◽  
Dimensional Structure ◽  
X Ray ◽  
3 Dimensional ◽  
Rational Drug ◽  
Related Compounds ◽  
Maximum Occupancy

The discovery of antipicornavirus activity associated with disoxaril 1 and related compounds, and the elucidation of the 3-dimensional structure of human rhinovirus-14 and −1A has lead to the use of rational drug design in the search for more potent and broad spectrum agents. The use of volume maps based on the X-ray conformation of these compounds in human rhinovirus-14 has revealed space filling requirements for activity for this serotype which has been confirmed by the use of the programme CoMFA. The principle interactions of the compounds within the binding site appear hydrophobic in nature. These studies have shown that maximum occupancy of the binding site is associated with good antiviral activity.

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.

Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

2020 ◽  
Vol 26 (42) ◽  
pp. 7623-7640 ◽  
Author(s):  
Cheolhee Kim ◽  
Eunae Kim
Keyword(s):  
Drug Design ◽  
Tyrosine Kinase ◽  
Receptor Tyrosine Kinase ◽  
Synergistic Effects ◽  
Rational Drug Design ◽  
Computational Techniques ◽  
Biological Targets ◽  
Type Iii ◽  
Theoretical Approaches ◽  
Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

Recent Advances in the Rational Drug Design Based on Multi-target Ligands

2020 ◽  
Vol 27 (28) ◽  
pp. 4720-4740 ◽  
Author(s):  
Ting Yang ◽  
Xin Sui ◽  
Bing Yu ◽  
Youqing Shen ◽  
Hailin Cong
Keyword(s):  
Drug Resistance ◽  
Clinical Practice ◽  
Side Effects ◽  
Drug Design ◽  
Rational Drug Design ◽  
Health Conditions ◽  
Pharmacokinetic Parameters ◽  
Single Target ◽  
Rational Drug ◽  
Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

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