Discovery and Preclinical Pharmacology of an Oral Bromodomain and Extra-Terminal (BET) Inhibitor Using Scaffold-Hopping and Structure-Guided Drug Design

2021 ◽  
Author(s):  
Ashvinikumar V. Gavai ◽  
Derek Norris ◽  
George Delucca ◽  
David Tortolani ◽  
John S. Tokarski ◽  
...  
Keyword(s):  
Drug Design ◽  
Scaffold Hopping ◽  
Bet Inhibitor ◽  
Preclinical Pharmacology

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

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.

ADMETopt: A Web Server for ADMET Optimization in Drug Design via Scaffold Hopping

2018 ◽  
Vol 58 (10) ◽  
pp. 2051-2056 ◽  
Author(s):  
Hongbin Yang ◽  
Lixia Sun ◽  
Zhuang Wang ◽  
Weihua Li ◽  
Guixia Liu ◽  
...  
Keyword(s):  
Drug Design ◽  
Web Server ◽  
Scaffold Hopping

Substituted 4-hydroxy-1,2,3-triazoles: synthesis, characterization and first drug design applications through bioisosteric modulation and scaffold hopping approaches

MedChemComm ◽  
2015 ◽  
Vol 6 (7) ◽  
pp. 1285-1292 ◽  
Author(s):  
Agnese C. Pippione ◽  
Franco Dosio ◽  
Alex Ducime ◽  
Antonella Federico ◽  
Katia Martina ◽  
...  
Keyword(s):  
Drug Design ◽  
Scaffold Hopping ◽  
Lead Compounds

The hydroxytriazole system is here analysed and used to modulate acidic moieties present in lead compounds.

scholarly journals Discovery of novel fragments inhibiting O-acetylserine sulphhydrylase by combining scaffold hopping and ligand–based drug design

2018 ◽  
Vol 33 (1) ◽  
pp. 1444-1452 ◽  
Author(s):  
Joana Magalhães ◽  
Nina Franko ◽  
Giannamaria Annunziato ◽  
Martin Welch ◽  
Stephen K. Dolan ◽  
...  
Keyword(s):  
Drug Design ◽  
Scaffold Hopping

scholarly journals SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

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.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

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.

scholarly journals Bioisosteric Replacement as a Tool in Anti-HIV Drug Design

2020 ◽  
Vol 13 (3) ◽  
pp. 36 ◽  
Author(s):  
Alexej Dick ◽  
Simon Cocklin
Keyword(s):  
Drug Design ◽  
Chemical Space ◽  
Experimental Therapeutics ◽  
Scaffold Hopping ◽  
Bioisosteric Replacement ◽  
Hiv Drugs ◽  
Anti Hiv ◽  
Hiv 1

Bioisosteric replacement is a powerful tool for modulating the drug-like properties, toxicity, and chemical space of experimental therapeutics. In this review, we focus on selected cases where bioisosteric replacement and scaffold hopping have been used in the development of new anti-HIV-1 therapeutics. Moreover, we cover field-based, computational methodologies for bioisosteric replacement, using studies from our group as an example. It is our hope that this review will serve to highlight the utility and potential of bioisosteric replacement in the continuing search for new and improved anti-HIV drugs.

scholarly journals SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

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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