Proceedings of the Workshop on Computational Approaches to Linguistic Creativity - CALC '09

Background: In view of many current mosquito-borne diseases there is a need for the design of novel repellents. Objective: The objective of this article is to review the results of the researches carried out by the authors in the computer-assisted design of novel mosquito repellents. Methods: Two methods in the computational design of repellents have been discussed: a) Quantitative Structure Activity Relationship (QSAR) studies from a set of repellents structurally related to DEET using computed mathematical descriptors, and b) Pharmacophore based modeling for design and discovery of novel repellent compounds including virtual screening of compound databases and synthesis of novel analogues. Results: Effective QSARs could be developed using mathematical structural descriptors. The pharmacophore based method is an effective tool for the discovery of new repellent molecules. Conclusion: Results reviewed in this article show that both QSAR and pharmacophore based methods can be used to design novel repellent molecules.

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Recent Advances and Computational Approaches in Peptide Drug Discovery

Current Pharmaceutical Design ◽

10.2174/1381612825666190911161106 ◽

2019 ◽

Vol 25 (31) ◽

pp. 3358-3366 ◽

Cited By ~ 2

Author(s):

Neha S. Maurya ◽

Sandeep Kushwaha ◽

Ashutosh Mani

Keyword(s):

Docking Studies ◽

Computational Approaches ◽

Simulation Tools ◽

Drug Candidates ◽

Recent Advances ◽

Long Duration ◽

Stability Of Complexes ◽

Therapeutic Peptides ◽

Target Binding ◽

Druggable Targets

Background: Drug design and development is a vast field that requires huge investment along with a long duration for providing approval to suitable drug candidates. With the advancement in the field of genomics, the information about druggable targets is being updated at a fast rate which is helpful in finding a cure for various diseases. Methods: There are certain biochemicals as well as physiological advantages of using peptide-based therapeutics. Additionally, the limitations of peptide-based drugs can be overcome by modulating the properties of peptide molecules through various biomolecular engineering techniques. Recent advances in computational approaches have been helpful in studying the effect of peptide drugs on the biomolecular targets. Receptor – ligand-based molecular docking studies have made it easy to screen compatible inhibitors against a target.Furthermore, there are simulation tools available to evaluate stability of complexes at the molecular level. Machine learning methods have added a new edge by enabling accurate prediction of therapeutic peptides. Results: Peptide-based drugs are expected to take over many popular drugs in the near future due to their biosafety, lower off-target binding chances and multifunctional properties. Conclusion: This article summarises the latest developments in the field of peptide-based therapeutics related to their usage, tools, and databases.

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EHAI: Enhanced Human Microbe-Disease Association Identification

Current Protein and Peptide Science ◽

10.2174/1389203721666200702150249 ◽

2020 ◽

Vol 21 (11) ◽

pp. 1078-1084

Author(s):

Ruizhi Fan ◽

Chenhua Dong ◽

Hu Song ◽

Yixin Xu ◽

Linsen Shi ◽

...

Keyword(s):

Microbial Community ◽

Human Health ◽

Complex Diseases ◽

Disease Association ◽

Computational Results ◽

Computational Approaches ◽

Disease Diagnostics ◽

Disease Associations ◽

Association Discovery ◽

Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

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