In silico SNP analysis and bioinformatics tools: a review of the state of the art to aid drug discovery

2011 ◽  
Vol 16 (17-18) ◽  
pp. 800-809 ◽  
Author(s):  
James T.L. Mah ◽  
Esther S.H. Low ◽  
Edmund Lee
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.


2011 ◽  
Vol 999 (999) ◽  
pp. 1-29
Author(s):  
Jeremy N. Burrows ◽  
Kelly Chibale ◽  
Timothy N.C. Wells

2020 ◽  
Vol 40 (04) ◽  
pp. 524-535
Author(s):  
Dmitry Y. Nechipurenko ◽  
Aleksey M. Shibeko ◽  
Anastasia N. Sveshnikova ◽  
Mikhail A. Panteleev

AbstractComputational physiology, i.e., reproduction of physiological (and, by extension, pathophysiological) processes in silico, could be considered one of the major goals in computational biology. One might use computers to simulate molecular interactions, enzyme kinetics, gene expression, or whole networks of biochemical reactions, but it is (patho)physiological meaning that is usually the meaningful goal of the research even when a single enzyme is its subject. Although exponential rise in the use of computational and mathematical models in the field of hemostasis and thrombosis began in the 1980s (first for blood coagulation, then for platelet adhesion, and finally for platelet signal transduction), the majority of their successful applications are still focused on simulating the elements of the hemostatic system rather than the total (patho)physiological response in situ. Here we discuss the state of the art, the state of the progress toward the efficient “virtual thrombus formation,” and what one can already get from the existing models.


2011 ◽  
Vol 11 (10) ◽  
pp. 1226-1254 ◽  
Author(s):  
Jeremy N. Burrows ◽  
Kelly Chibale ◽  
Timothy N.C. Wells

2002 ◽  
Vol 20 (4) ◽  
pp. 305-309 ◽  
Author(s):  
Sean Ekins ◽  
John Rose
Keyword(s):  

Author(s):  
José T. Moreira-Filho ◽  
Rafael F. Dantas ◽  
Mário R. Senger ◽  
Arthur C. Silva ◽  
Dulcinea M.B. Campos ◽  
...  

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 (SyntaLinker) 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 SyntaLinker 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 SyntaLinkercan be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.


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