scholarly journals Selected Approaches for Rational Drug Design and High Throughput Screening to Identify Anti-Cancer Molecules

2012 ◽  
Vol 12 (9) ◽  
pp. 1143-1155 ◽  
Author(s):  
Michael Hedvat ◽  
Luni Emdad ◽  
Swadesh K. Das ◽  
Keetae Kim ◽  
Santanu Dasgupta ◽  
...  
Keyword(s):  
Drug Design ◽  
High Throughput ◽  
High Throughput Screening ◽  
Rational Drug Design ◽  
Rational Drug ◽  
Anti Cancer

Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents

2012 ◽  
Vol 20 (15) ◽  
pp. 4848-4855 ◽  
Author(s):  
Alejandro Speck-Planche ◽  
Valeria V. Kleandrova ◽  
Feng Luan ◽  
M. Natália D.S. Cordeiro
Keyword(s):  
Colorectal Cancer ◽  
Drug Design ◽  
Cancer Chemotherapy ◽  
In Silico ◽  
Rational Drug Design ◽  
Rational Drug ◽  
Anti Cancer ◽  
Qsar Models

scholarly journals Type II topoisomerases as targets for rational drug design

2014 ◽  
Vol 70 (a1) ◽  
pp. C713-C713
Author(s):  
Ivan Laponogov ◽  
Dennis Veselkov ◽  
Trishant Umrekar ◽  
Isabelle Crevel ◽  
Xiao-su Pan ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Drug Design ◽  
Dna Supercoiling ◽  
Rational Drug Design ◽  
Human Organism ◽  
Type Ii ◽  
Double Stranded Dna ◽  
Rational Drug ◽  
Bacterial Drug Resistance ◽  
Anti Cancer

Bacterial drug resistance is a growing and now widely recognised threat and the limited number of new antibacterials developed in the recent years is a serious matter of concern. One of the approaches to combat this growing threat is to investigate the mechanisms of action of currently available antibacterials, as well as studying the way that bacteria are currently developing drug resistance and may potentially in the future develop drug resistance to known remedies. This knowledge should in turn be used in rational drug design and the general development of the appropriate frameworks for combating bacteria, while at the same time keeping the negative side effects of the drugs to the acceptable minimum. This is especially important when both bacteria and humans share similar drug targets, such as is the case for topoisomerases (in humans, the latter are also targeted by anti-cancer drugs). Our main protein targets of interest are type II topoisomerases which are involved in regulation of the DNA supercoiling in both bacteria and eukaryotes and also in decatenation of bacterial daughter chromosomes during cell division. Type II topoisomerases are performing their biological action by binding the double stranded DNA (called G-segment or Gate-DNA), temporarily cleaving it and passing another double stranded DNA (called T-segment) in the ATP-assisted process via the cleavage region thus changing the linking number in steps of12. After that the G-segment is resealed and released. Several drugs were found to be able to disrupt this process ultimately resulting in the cell death (thus having anti-bacterial or anti-cancer action). Here we present our studies of the protein-DNA-drug interactions which are involved in the action of currently clinically used quinolone antibacterials as well as their newly developed alternatives (such as quinazolinediones) in relation to their mechanism of action and already established potential routes for developing drug resistance in bacteria. We present the results for different pathogens (including, but not limited to Streptococcus pneumoniae and Klebsiella pneumoniae) and also compare the configuration of the active site with the one from type II topoisomerases from a human organism.

Rational Drug Design of G-Quartet DNA as Anti-Cancer Agents

2005 ◽  
Vol 11 (22) ◽  
pp. 2841-2854 ◽  
Author(s):  
N. Jing ◽  
W. Sha ◽  
Y. Li ◽  
W. Xiong ◽  
D. Tweardy
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Rational Drug ◽  
Anti Cancer

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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