An Approach of Computer-Aided Drug Design (CADD) Tools for In Silico Pharmaceutical Drug Design and Development

Hoque, Injamul; Chatterjee, Arkendu; Bhattacharya, Somenath; Biswas, Raj

doi:10.22192/ijarbs.2017.04.02.009

Cited by 28 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CADD Methods are Used for: Target structure analysis (possible binding site detection), candidate molecule generation, docking of generated molecules with target, give them rank according to bio affinities and optimization of molecules for further improvement 19,20 . CADD applied in the field of Research and development, target identification validation and preclinical study (Pharmacokinetic; ADMET prediction).…”

Section: Applications Of Caddmentioning

confidence: 99%

Untitled

2018

IJPSR

View full text Add to dashboard Cite

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).

show abstract

Section: Applications Of Caddmentioning

confidence: 99%

Untitled

2018

IJPSR

View full text Add to dashboard Cite

show abstract

“…Structure-based drug design is an approach that is based on geometric and chemical structural information from proteins. Ligand-based drug design is a computer-aided approach based on information from ligands and is used when 3D receptor information is not available [8]. Basically, drug design is carried out on information from the structure of the protein to look for suitable ligands [9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein-Ligand Binding Site for Drug Design Using Context Relevant Self Organizing Maps (CRSOM)

Imah¹,

Wulandari²

2020

IJIES

View full text Add to dashboard Cite

Research on binding sites has been done to find suitable ligands to treat a particular disease. The binding site is a pocket on the surface of the protein, which acts as a place to attach a ligand. In bioinformatics, searching for binding sites is applied to drug design problems. Currently, computer-aided drug design has been developed. In this study, the prediction of protein-ligand binding sites formulated as a binary classification, which is distinguish the location that has potential to binding the ligand and the location that has no potential to binding the ligand. The dataset that will be used in this research is taken from the RCSB Protein Data Bank of 14 proteins data. The classification method used in this research is Context Relevant Self Organizing Maps (CRSOM), where the CRSOM method gives higher accuracy results compared to Backpropagation and Deep Learning. Context Relevant Self Organizing Maps (CRSOM) is chosen as a supervised learning classification algorithm that has an optimal internal representation, where data belonging different classes are separated with wider margin, while data belonging to the same class are clustered closely to each other. Thus, CRSOM is able to visualize high-dimensional protein data into binding site and non-binding site classes significantly. The results of the study obtained an average training accuracy of 99,60%, testing accuracy of 96.26%, and the average test time of 28.63 seconds, the result is better than the predecessor.

show abstract

“…A quick and efficient way to find a new drug candidate is through computer-aided drug design (CADD) which is a powerful tool used to find new compound by reducing risk, time, and cost of research in the drug discovery process (Baig et al 2016;Bisht & Singh 2018;Ferreira et al 2015;Hoque et al 2017). Moreover, in February 2020, the first high-resolution crystal structure of the main protease of SARS-CoV-2 was published (PDB code: 6lu7) (Liu et al 2020b).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #3 of "Tackling the SARS-CoV-2 main protease using hybrid derivatives of 1,5-disubstituted tetrazole-1,2,3-triazoles: an in silico assay (v0.1)"

Peele¹

2020

View full text Add to dashboard Cite

In regard to the actual public health global emergency and, based on the state of the art about the ways to inhibit the SARS-CoV-2 treating the COVID19, a family of 1,5disubstituted tetrazole-1,2,3-triazoles, previously synthesized, have been evaluated through in silico assays against the main protease of the mentioned virus (CoV-2-M Pro). The results show that three of these compounds present a more favorable interaction with the selected target than the co-crystallized molecule, which is a peptide-like derivative. It was also found that also hydrophobic interactions play a key role in the ligand-target molecular couplings, due to the higher hydrophobic surfaces into the active site. Finally, a pharmacophore model has been proposed based on the results below, and a family of 1,5-DT derivatives has been designed and tested with the same methods employed in this work. It was concluded that the compound with the isatin as a substituent (P8) present the higher ligand-target interaction, which makes this a strong drug candidate against COVID19, due can inhibit the CoV-2-M Pro protein.

show abstract

An Approach of Computer-Aided Drug Design (CADD) Tools for In Silico Pharmaceutical Drug Design and Development

Cited by 28 publications

References 10 publications

Untitled

Untitled

Predicting Protein-Ligand Binding Site for Drug Design Using Context Relevant Self Organizing Maps (CRSOM)

Peer Review #3 of "Tackling the SARS-CoV-2 main protease using hybrid derivatives of 1,5-disubstituted tetrazole-1,2,3-triazoles: an in silico assay (v0.1)"

Contact Info

Product

Resources

About