The State of the Art in Anti-Malarial Drug Discovery and Development

2011 ◽  
Vol 11 (10) ◽  
pp. 1226-1254 ◽  
Author(s):  
Jeremy N. Burrows ◽  
Kelly Chibale ◽  
Timothy N.C. Wells
2011 ◽  
Vol 999 (999) ◽  
pp. 1-29
Author(s):  
Jeremy N. Burrows ◽  
Kelly Chibale ◽  
Timothy N.C. Wells

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.


2001 ◽  
Vol 7 (S2) ◽  
pp. 622-623
Author(s):  
Xiaoyou Ying ◽  
Jean Sprinkle Cavallo ◽  
Bruce McCullough

Digital microscopy, the integration of digital and microscopy technologies, was initiated for quantitative microscopic image analysis, but it is now for almost all microscopy applications. During the past decade, with the advance of digital technologies, digital microscopy imaging is becoming an indispensable technology in drug discovery.We started establishing state-of-the-art digital microscopy imaging for drug discovery with the investigation of bioimaging applications at our US research site. Our results shown that all the top 5 bioimaging needs require computer-aided microscopy. Based on this investigation and our review of the microscopy imaging applications in the pharmaceutical industry, we determined four directions for microscopy in drug discovery: multidimensional/multimodal microscopy, digitalization, automation, and bioimage informatics.Multidimensional/multimodal microscopy imaging is required by the nature of biological research, which is fundamental in drug discovery. From genomic imaging to pathology observation, we require biological details and compound activities at the levels from subcellular organelles to organ tissues, from cellular signaling to anatomical locations of compounds.


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.


2022 ◽  
Vol 74 (1) ◽  
pp. 141-206
Author(s):  
Sonia Youhanna ◽  
Aurino M. Kemas ◽  
Lena Preiss ◽  
Yitian Zhou ◽  
Joanne X. Shen ◽  
...  

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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