Computational drug discovery

Ouyang, Sisheng; Lu, Junyan; Kong, Xiangqian; Liang, Zhongjie; Luo, Cheng; Jiang, Hualiang

doi:10.1038/aps.2012.109

Cited by 300 publications

(196 citation statements)

References 90 publications

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“… Evaluation of in-vivo experiments, bioinformatics analysis, etc.  Preclinical evaluation 30,31,32 .…”

Section: Fig 4: Approach Of Drug Design With Unknown Targetmentioning

confidence: 99%

“…Through using MOEA, Cyndi searches the conformational space in constant time, also controls geometric diversity as well as energy accessibility. Another one is Macro Model integrated in MaestroV7.5 (Schrodinger Inc.) which is different from Cyndi in terms of sampling depth of conformational space and the conformational cost 73,74 . Some examples of conformational search algorithms have been shown in Table 1.…”

Section: Conformation Generation Through Caddmentioning

confidence: 99%

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Untitled

2018

IJPSR

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ABSTRACT:The process of drug development and drug discovery is very challenging, expensive and time consuming. It has been accelerated due to development of computational tools and methods. Over the last few years, computer aided drug design (CADD) also known as in silico screening has become a powerful technique because of its utility in various phases of drug discovery and development through various advanced features. In silico screening also paves path for the synthesis and screening of selected compounds for better therapeutics. This review focuses on computational chemistry and computer aided drug discovery which are aimed to cover a wide range of computational approaches including new methodologies as well as practical aspects in this area. This review provides an insight about the developmental chain, approaches and applications of CADD; various data sources; computational methods for the discovery of new molecular entities; clinically approved drugs developed through CADD; and also summarizes the crucial steps of in silico drug designing like homology modelling, docking, multi-target searching and design, pharmacophore development, conformation generation and quantitative structure activity relationship (QSAR).

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“… Evaluation of in-vivo experiments, bioinformatics analysis, etc.  Preclinical evaluation 30,31,32 .…”

Section: Fig 4: Approach Of Drug Design With Unknown Targetmentioning

confidence: 99%

Section: Conformation Generation Through Caddmentioning

confidence: 99%

Untitled

2018

IJPSR

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“…The computational methods employed in drug discovery can be classified into two main approaches: ligand-based drug design (LBDD) and structure-based drug design (SBDD) [15][16][17]. The first approach is applicable in the absence of information regarding the 3D structure of target molecules [16].…”

Section: Introductionmentioning

confidence: 99%

Geometry optimization of steroid sulfatase inhibitors - the influence on the free binding energy with STS

et al. 2017

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In the paper we review the application of two techniques (molecular mechanics and quantum mechanics) to study the influence of geometry optimization of the steroid sulfatase inhibitors on the values of descriptors coded their chemical structure and their free binding energy with the STS protein. We selected 22 STS-inhibitors and compared their structures optimized with MM+, PM7 and DFT B3LYP/6-31++G* approaches considering separately the bond lengths, angles, dihedral angles and total energies. We proved that different minimum energy conformers could be generated depending on the choice of the optimization method. However, the results indicated that selection of the geometry optimization method did not affect the optimal STS inhibitor coordinates, and hence the values of molecular descriptors which describe the 3D structure of the molecule. To study the interaction pattern of the STS inhibitors (optimized using different methods) with the target receptor we applied two strategies: AutoDock and PathDock. The docking studies point out that selection of software to docking simulation is one of the crucial factors determining the binding mode of STS inhibitors with their molecular target. Other factor is related to the ligand orientation in the binding pocket. Finally, obtained results indicate that MM+ and PM7 methods (faster and less expensive) could be successfully employed to geometry optimization of the STS inhibitors before their docking procedure as well as for molecular descriptors calculations.

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“…The structure-based drug design (SBDD) methods, such as molecular docking and de novo drug design, depend on the knowledge of the structure of the target macromolecule, which are mainly obtained from crystal structures, NMR data and homology models [17].…”

Section: Introductionmentioning

confidence: 99%

Structure Prediction and in Silico Designing of Drugs Against Kallikrein Protein 12

Devgan¹

2017

Int J Curr Pharm Sci

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Objective: Human Kallikrein protein 12 (hK12) might serve as a novel diagnostic and prognostic biomarker, as well as a potential therapeutic target, in gastric cancer. Methods:In this work, a theoretical model of hK12 receptor protein was generated using the concepts of homology modeling and loop modeling. The resulting model was validated with Ramachandran plot analysis. The ligands generated with the help of Drug bank were docked against hK12 receptor protein using AutoDock Vina in PyRx 0.8. The structure of ligand DB04786 (Suramin), with least binding energy, was varied by using ACD/ChemSketch 8.0 and the docking was done for the resulting 16 new ligands. Results:The results indicated that the ligand10 bears the minimum binding energy (-12.3 Kcal/mol) with the target protein and thus the prospects of binding are high. The results also clearly demonstrated that the in silico molecular docking studies of selected ligands, i.e., suramin, ligands 5, 6, 10 and 16 with hK12 protein exhibited favourable binding interactions and warranted. Conclusion:Further studies needed for the development of potent inhibitors for the overexpression of hK12 protein making the management of gastric cancer more efficient.

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Computational drug discovery

Cited by 300 publications

References 90 publications

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Geometry optimization of steroid sulfatase inhibitors - the influence on the free binding energy with STS

Structure Prediction and in Silico Designing of Drugs Against Kallikrein Protein 12

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