Grid-Based Technologies for In Silico Screening and Drug Design

Various techniques for rational drug design are presented in the paper. The methods are based on a substitution of antipharmacophore atoms of the molecules of training dataset by new atoms and/or group of atoms increasing the atomic bioactivity increments obtained from an SAR study. Furthermore, a design methodology based on the genetic algorithm DesPot for discrete optimization and generation of new drug candidate structures is described. Additionally, wide spectra of SAR approaches (3D/4D QSAR interior and exterior-based methods – BiS, CiS, ConGO, CoMIn, high-quality docking method - ReDock) using MERA force field and/or AlteQ quantum chemical method for correct prognosis of bioactivity and the bioactive probability have been described. The design methods are implemented at www.chemosophia.com web-site for online computational services.

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High throughput in-silico screening of large ligand databases for rational drug design

Modern Methods for Theoretical Physical Chemistry of Biopolymers ◽

10.1016/b978-044452220-7/50073-3 ◽

2006 ◽

pp. 179-189

Author(s):

H. Merlitz ◽

B. Fischer ◽

W. Wenzel

Keyword(s):

Drug Design ◽

High Throughput ◽

In Silico ◽

Rational Drug Design ◽

In Silico Screening ◽

Rational Drug

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Internet Based Drug Design of New Opioid Analgesics

International Journal of Recent Advances in Science and Technology ◽

10.30750/ijarst.531 ◽

2018 ◽

Vol 5 (3) ◽

Author(s):

Avnish Kaur ◽

Meenakshi Mehra ◽

Mumtaz Ahmed

Keyword(s):

Drug Design ◽

Therapeutic Potential ◽

Opioid Analgesic ◽

Opioid Analgesics ◽

Rational Drug Design ◽

Input Structure ◽

Rational Drug ◽

Structural Activity Relationship ◽

Sar Study ◽

Use Of Internet

The development of new drug with its therapeutic potential is one of the most vital process in pharmaceutical industry. Now a day, there is development and importance of computational chemistry including molecular docking and a SAR study which deals with pharmacophore based drug design approach. Also, the methodology linked with modification of the target based drug discovery has been performed by using various computational tools. Thus, the present study deals with the Structural Activity Relationship study and pharmacophore based drug design approaches with the use of internet based tools which are free of cost and compatible with any platform. Here, attempts are made to design OPIORPHIN analogue by pharmacophore study to design more potent or equivalent opioid analgesic using free internet based tools by using Java platform to input structure, calculate its drug likeness, molecular properties and toxicity which are important parameters for structure based rational drug design.

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Identification of SENP1 inhibitors through in silico screening and rational drug design

European Journal of Medicinal Chemistry ◽

10.1016/j.ejmech.2016.06.018 ◽

2016 ◽

Vol 122 ◽

pp. 178-184 ◽

Cited By ~ 6

Author(s):

Yaxue Zhao ◽

Zhongli Wang ◽

Jianchen Zhang ◽

Huchen Zhou

Keyword(s):

Drug Design ◽

In Silico ◽

Rational Drug Design ◽

In Silico Screening ◽

Rational Drug

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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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