Advanced Computer‐Assisted Techniques in Drug Discovery

2014 ◽  
Vol 13 (2) ◽  
pp. 87-108 ◽  
Author(s):  
Pierfausto Seneci ◽  
Giorgio Fassina ◽  
Vladimir Frecer ◽  
Stanislav Miertus

Abstract The review will focus on the aspects of combinatorial chemistry and technologies that are more relevant in the modern pharmaceutical process. An historical, critical introduction is followed by three chapters, dealing with the use of combinatorial chemistry/high throughput synthesis in medicinal chemistry; the rational design of combinatorial libraries using computer-assisted combinatorial drug design; and the use of combinatorial technologies in biotechnology. The impact of “combinatorial thinking” in drug discovery in general, and in the examples reported in details, is critically discussed. Finally, an expert opinion on current and future trends in combinatorial chemistry and combinatorial technologies is provided.


2020 ◽  
Vol 21 ◽  
Author(s):  
Paranjeet Kaur ◽  
Gopal Khatik

Background: In this fast-growing era, high throughput data is now being so easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, antidiabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of antidiabetic agents. Results: To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction. Conclusion: Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatics tools in the discovery of antidiabetic agents keeping in-view of the growing importance for treating diabetes.


SMPTE Journal ◽  
1982 ◽  
Vol 91 (10) ◽  
pp. 931-933 ◽  
Author(s):  
M. H. Jones ◽  
D. A. Tilsley ◽  
B. J. Roche

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