Computational Approaches in Drug Designing and Their Applications

Singh, Dev Bukhsh; Pathak, Rajesh Kumar

doi:10.1007/978-1-0716-0607-0_6

Cited by 17 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, many of the computational approaches are widely used in the discovery and development of new drug candidates [1,2]. Most of these approaches were supported by the advances in macromolecular X-ray crystallography.…”

Section: Introductionmentioning

confidence: 99%

Icotinib, Almonertinib, and Olmutinib: A 2D Similarity/Docking-Based Study to Predict the Potential Binding Modes and Interactions into EGFR

et al. 2021

View full text Add to dashboard Cite

In the current study, a 2D similarity/docking-based study was used to predict the potential binding modes of icotinib, almonertinib, and olmutinib into EGFR. The similarity search of icotinib, almonertinib, and olmutinib against a database of 154 EGFR ligands revealed the highest similarity scores with erlotinib (0.9333), osimertinib (0.9487), and WZ4003 (0.8421), respectively. In addition, the results of the docking study of the three drugs into EGFR revealed high binding free energies (Gb = −6.32 to −8.42 kcal/mol) compared to the co-crystallized ligands (Gb = −7.03 to −8.07 kcal/mol). Analysis of the top-scoring poses of the three drugs was done to identify their potential binding modes. The distances between Cys797 in EGFR and the Michael acceptor sites in almonertinib and olmutinib were determined. In conclusion, the results could provide insights into the potential binding characteristics of the three drugs into EGFR which could help in the design of new more potent analogs.

show abstract

Section: Introductionmentioning

confidence: 99%

Icotinib, Almonertinib, and Olmutinib: A 2D Similarity/Docking-Based Study to Predict the Potential Binding Modes and Interactions into EGFR

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The wild type and variant DGAT1 proteins' RMSDs were calculated to predict their respective stabilities. The RMSD discrepancies created during MD simulation can be used to measure the protein's stability in relation to its structure 20 , 21 . The RMSD deviations observed during the MD simulation can be used to measure the protein's stability in relation to its structure.…”

Section: Resultsmentioning

confidence: 99%

Unraveling structural and conformational dynamics of DGAT1 missense nsSNPs in dairy cattle

Pathak

Lim

Park

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Cattle are domestic animals that have been nourishing humans for thousands of years. Milk from cattle represents a key source of high-quality protein, fat, and other nutrients. The nutritional value of milk and dairy products is closely associated with the fat content, providing up to 30% of the total fat consumed in the human diet. The fat content in cattle milk represents a major concern for the scientific community due to its association with human health. The relationship between milk fat content and diacylglycerol o-acyltransferase 1 gene (DGAT1) is well described in literature. Several studies demonstrated the difference in fat contents and other milk production traits in a wide range of cattle breeds, to be associated with missense non-synonymous single nucleotide polymorphisms (nsSNPs) of the DGAT1 gene. As a result, an nsSNPs analysis is crucial for unraveling the DGAT1 structural and conformational dynamics linked to milk fat content. DGAT1-nsSNPs are yet to be studied in terms of their structural and functional impact. Therefore, state-of-the-art computational and structural genomic methods were used to analyze five selected variants (W128R, W214R, C215G, P245R, and W459G), along with the wild type DGAT1. Significant structural and conformational changes in the variants were observed. We illustrate how single amino acid substitutions affect DGAT1 function, how this contributes to our understanding of the molecular basis of variations in DGAT1, and ultimately its impact in improving fat quality in milk.

show abstract

“…Drug design involves the identification of candidate drug molecules complementary to the target with which it can interact and bind. It involves different bioinformatics approaches such as target structure modeling, binding site/cavity or pocket related predictions, substructure or similar structure searching, ligand designing, lead optimization, pharmacophore analysis, protein‐ligand docking, and virtual screening, molecular dynamics simulations, QSAR analysis, absorption, distribution, metabolism, excretion and toxicity (ADMET) related analysis (Singh, 2020; Singh & Pathak, 2020). Modern drug development and discovery‐related analysis are based on various tools/software designed using ML approaches.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches and their applications in drug discovery and design

et al. 2022

Self Cite

View full text Add to dashboard Cite

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

show abstract

Computational Approaches in Drug Designing and Their Applications

Cited by 17 publications

References 21 publications

Icotinib, Almonertinib, and Olmutinib: A 2D Similarity/Docking-Based Study to Predict the Potential Binding Modes and Interactions into EGFR

Icotinib, Almonertinib, and Olmutinib: A 2D Similarity/Docking-Based Study to Predict the Potential Binding Modes and Interactions into EGFR

Unraveling structural and conformational dynamics of DGAT1 missense nsSNPs in dairy cattle

Machine learning approaches and their applications in drug discovery and design

Contact Info

Product

Resources

About