Computer-Aided Rational Drug Design:  A Novel Agent (SR13668) Designed to Mimic the Unique Anticancer Mechanisms of Dietary Indole-3-Carbinol to Block Akt Signaling

By and by the world is in a battle with throat and lung infections with no prompt medicines accessible. The scourge brought about by these infections is expanding step by step. A ton of researchers are continuing for the medication up-and-comer that could help the medical care framework in this battle. We present a docking?based screening using a quantum mechanical scoring of a library built from approved drugs viz. Meropenem, Cefixime, Curcumin, Clindamycin, Cefaclor, Cefadroxil with Proteins of several infections causing microbes that could display antimicrobial activity against these infections.. We hope that these findings may contribute to the rational drug design against these infections.

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Tomocomd-Cardd, a novel approach for computer-aided ? rational? drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-004-5171-y ◽

2004 ◽

Vol 18 (10) ◽

pp. 615-634 ◽

Cited By ~ 50

Author(s):

Yovani Marrero-Ponce ◽

Juan A. Castillo-Garit ◽

Ervelio Olazabal ◽

Hector S. Serrano ◽

Alcidez Morales ◽

...

Keyword(s):

Drug Design ◽

In Silico ◽

Rational Drug Design ◽

Promising Method ◽

Experimental Assessment ◽

In Silico Design ◽

Computational Screening ◽

Novel Approach ◽

Rational Drug ◽

Computer Aided

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Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

Arhiv za farmaciju ◽

10.5937/arhfarm71-32523 ◽

2021 ◽

Vol 71 (4) ◽

pp. 225-256

Author(s):

Milica Radan ◽

Jelena Bošković ◽

Vladimir Dobričić ◽

Olivera Čudina ◽

Katarina Nikolić

Keyword(s):

Drug Discovery ◽

Side Effects ◽

Drug Design ◽

Inflammatory Diseases ◽

Rational Drug Design ◽

Computer Aided Drug Design ◽

Drug Discovery And Development ◽

Rational Drug ◽

Computer Aided ◽

Anti Inflammatory

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

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Ligand-based design of anticancer MMP2 inhibitors: a review

Future Medicinal Chemistry ◽

10.4155/fmc-2021-0262 ◽

2021 ◽

Author(s):

Saptarshi Sanyal ◽

Sk Abdul Amin ◽

Nilanjan Adhikari ◽

Tarun Jha

Keyword(s):

Clinical Trials ◽

Drug Design ◽

Quantitative Structure Activity Relationship ◽

Rational Drug Design ◽

Quantitative Structure ◽

Matrix Metalloproteinase Inhibitors ◽

Structure Activity ◽

Rational Drug ◽

Metalloproteinase Inhibitors ◽

Computer Aided

MMP2, a Zn2+-dependent metalloproteinase, is related to cancer and angiogenesis. Inhibition of this enzyme might result in a potential antimetastatic drug to leverage the anticancer drug armory. In silico or computer-aided ligand-based drug design is a method of rational drug design that takes multiple chemometrics (i.e., multi-quantitative structure–activity relationship methods) into account for virtually selecting or developing a series of probable selective MMP2 inhibitors. Though existing matrix metalloproteinase inhibitors have shown plausible pan-matrix metalloproteinases (MMP) activity, they have resulted in various adverse effects leading to their being rescinded in later phases of clinical trials. Therefore a review of the ligand-based designing methods of MMP2 inhibitors would result in an explicit route map toward successfully designing and synthesizing novel and selective MMP2 inhibitors.

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

10.26434/chemrxiv.9866912 ◽

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

Introduction: 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. Objectives: 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. Methods: 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. Results: 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. Conclusions: 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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Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

Current Medicinal Chemistry ◽

10.2174/0929867325666180622143548 ◽

2020 ◽

Vol 26 (42) ◽

pp. 7623-7640 ◽

Cited By ~ 1

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.

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

Current Medicinal Chemistry ◽

10.2174/0929867327666200102120652 ◽

2020 ◽

Vol 27 (28) ◽

pp. 4720-4740 ◽

Cited By ~ 3

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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