The application of bioisosteres in drug design for novel drug discovery: focusing on acid protease inhibitors

2012 ◽  
Vol 7 (10) ◽  
pp. 903-922 ◽  
Author(s):  
Yoshio Hamada ◽  
Yoshiaki Kiso
2021 ◽  
Vol 01 (1) ◽  
pp. 6-8
Author(s):  
Rafik Karaman

Imagination is more important than knowledge when knowledge is limited and cannot solve important questions. Inventiveness in the drug design has been clumsiness in quality and quantity. This may be due to the ineptness and incapability of medicinal chemists to comprehend biochemistry and biology issues. On the other hand, biochemists, biologists, and pharmaceutical chemists do not possess the expertise to make complex organic entities. Hence, a team comprising of all expertise is a must to invoke a novel drug. Drug discovery and development is expensive and time-consuming since it consists of many steps that start with target and lead discovery and end with human clinical trials. The estimation is that about 10-15 years are needed to present a new drug to the market with a cost of 1-1.5 billion dollars (Figure 1), (Karaman 2014 a,b). During the recent few decades, considerable attention has been focused on improving the pharmacokinetics of existing marketed drugs, thus providing new organic entities capable of providing more e!ciency with fewer drawbacks than their corresponding parent drugs. Among the approaches that can fulfill the requirements for invoking therapeutics with optimum absorption, distribution, metabolism, and excretion (ADME) properties is the prodrug approach.


2020 ◽  
Author(s):  
Jasper Kyle Catapang ◽  
Junie B. Billones

SARS-CoV-2 has no known vaccine nor any effective treatment that has been released for clinical trials yet. This has ultimately paved the way for novel drug discovery approaches since although there are multiple efforts focused on drug repurposing of clinically-approved drugs for SARS-CoV-2, it is also worth considering that these existing drugs can be surpassed in effectivity by novel ones. This research focuses on the generation of novel candidate inhibitors via constrained graph variational autoencoders and the calculation of their Tanimoto similarities against existing drugs---repurposing these existing drugs and considering the novel ligands as possible SARS-CoV-2 main protease inhibitors and ACE2 receptor blockers by docking them through PyRx and ranking these ligands.


2020 ◽  
Author(s):  
Jasper Kyle Catapang ◽  
Junie B. Billones

SARS-CoV-2 has no known vaccine nor any effective treatment that has been released for clinical trials yet. This has ultimately paved the way for novel drug discovery approaches since although there are multiple efforts focused on drug repurposing of clinically-approved drugs for SARS-CoV-2, it is also worth considering that these existing drugs can be surpassed in effectivity by novel ones. This research focuses on the generation of novel candidate inhibitors via constrained graph variational autoencoders and the calculation of their Tanimoto similarities against existing drugs---repurposing these existing drugs and considering the novel ligands as possible SARS-CoV-2 main protease inhibitors and ACE2 receptor blockers by docking them through PyRx and ranking these ligands. Additionally, this research has successfully generated three novel ligands for the SARS-CoV-2 main protease and four novel ligands for the ACE2 receptor.


2017 ◽  
Vol II (I) ◽  
pp. 1-8
Author(s):  
Arif Paiman ◽  
Ahmad Mohammad ◽  
Mubashar Rehman

In modern day, Data on different diseases and drug substances with their properties like modification, side effects, and dose requires documentation data and building library exploring, such library with vast information in every aspect needs computational methods used in CADD. Recognition of specific targets for the drug tested and defining pharmacological activity of a drug candidate based on the structure of both drug and its target, finding outside effects of drugs at the molecular level and calculation of toxicity caused by metabolism of drug applications of Computer aided drug design in the drug discovery process. We can get additional tools and websites which serve As a tool for the source of data and computational drug design are available on the web interface and being used extensively by researchers and scientists to save time and budget for speeding up the process of experiments for Novel Drug compound.


2020 ◽  
Author(s):  
Jasper Kyle Catapang ◽  
Junie B. Billones

SARS-CoV-2 has no known vaccine nor any effective treatment that has been released for clinical trials yet. This has ultimately paved the way for novel drug discovery approaches since although there are multiple efforts focused on drug repurposing of clinically-approved drugs for SARS-CoV-2, it is also worth considering that these existing drugs can be surpassed in effectivity by novel ones. This research focuses on the generation of novel candidate inhibitors via constrained graph variational autoencoders and the calculation of their Tanimoto similarities against existing drugs---repurposing these existing drugs and considering the novel ligands as possible SARS-CoV-2 main protease inhibitors and ACE2 receptor blockers by docking them through PyRx and ranking these ligands. Additionally, this research has successfully generated three novel ligands for the SARS-CoV-2 main protease and four novel ligands for the ACE2 receptor.


2020 ◽  
Author(s):  
Jasper Kyle Catapang ◽  
Junie B. Billones

SARS-CoV-2 has no known vaccine nor any effective treatment that has been released for clinical trials yet. This has ultimately paved the way for novel drug discovery approaches since although there are multiple efforts focused on drug repurposing of clinically-approved drugs for SARS-CoV-2, it is also worth considering that these existing drugs can be surpassed in effectivity by novel ones. This research focuses on the generation of novel candidate inhibitors via constrained graph variational autoencoders and the calculation of their Tanimoto similarities against existing drugs---repurposing these existing drugs and considering the novel ligands as possible SARS-CoV-2 main protease inhibitors and ACE2 receptor blockers by docking them through PyRx and ranking these ligands.


RSC Advances ◽  
2015 ◽  
Vol 5 (42) ◽  
pp. 33058-33066 ◽  
Author(s):  
Bao Tu ◽  
Zhi-Feng Chen ◽  
Zhi-Juan Liu ◽  
Li-Yang Cheng ◽  
Yan-Jun Hu

The structure–activity relationship of the different flavones has been investigated, which may meaningful for drug discovery, and novel drug design.


2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.


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