Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds

Phillip R. Lazzara; Terry W. Moore

doi:10.1039/c9md00396g

Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds

RSC Medicinal Chemistry ◽

10.1039/c9md00396g ◽

2020 ◽

Vol 11 (1) ◽

pp. 18-29 ◽

Cited By ~ 2

Author(s):

Phillip R. Lazzara ◽

Terry W. Moore

Keyword(s):

Drug Metabolism ◽

Aromatic Compounds ◽

Medicinal Chemistry ◽

Lead Optimization ◽

Scaffold Hopping ◽

Oxidative Drug Metabolism

Mitigating oxidative drug metabolism is an important component of lead optimization. This review focuses on scaffold-hopping strategies used in the recent medicinal chemistry literature to address metabolic liabilities of aromatic compounds.

Download Full-text

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

10.26434/chemrxiv.12271508.v2 ◽

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.

Download Full-text

Faculty Opinions recommendation of Aryltriflates as a Neglected Moiety in Medicinal Chemistry: A Case Study from a Lead Optimization of CXCL8 Inhibitors.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13277000.14635111 ◽

2011 ◽

Author(s):

Jefferson Tilley

Keyword(s):

Medicinal Chemistry ◽

Lead Optimization

Download Full-text

Withdrawal Notice: Lead Optimization from Natural Ligands by In-Silico Approach for Discovering Potential Monoamine Oxidasesinhibitors (MAOIs)

Current Drug Metabolism ◽

10.2174/1389200221666200502014314 ◽

2020 ◽

Vol 21 ◽

Author(s):

Priyanka Dhiman ◽

Neelam Malik ◽

Anurag Khatkar

Keyword(s):

Drug Metabolism ◽

In Silico ◽

Editorial Policy ◽

Lead Optimization ◽

Editorial Policies ◽

Current Drug ◽

Natural Ligands ◽

Legal Right

The article has been withdrawn at the request of the authors and editor of the journal Current Drug Metabolism. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Download Full-text