Drug Metabolism Research as an Integral Part of the Drug Discovery Process

2007 ◽  
pp. 237-260
Author(s):  
W. Griffith Humphreys
Keyword(s):  
Drug Discovery ◽  
Drug Metabolism ◽  
Discovery Process ◽  
Drug Discovery Process

Cunninghamella Biotransformation - Similarities to Human Drug Metabolism and Its Relevance for the Drug Discovery Process

2016 ◽  
Vol 17 (2) ◽  
pp. 107-117 ◽  
Author(s):  
Kamil Piska ◽  
Dorota |elaszczyk ◽  
Marek Jamrozik ◽  
Paulina Kubowicz-Kwa|ny ◽  
El|bieta P|kala
Keyword(s):  
Drug Discovery ◽  
Drug Metabolism ◽  
Discovery Process ◽  
Drug Discovery Process

HPLC-API/MS/MS: a powerful tool for integrating drug metabolism into the drug discovery process

1997 ◽  
Vol 2 (12) ◽  
pp. 532-537 ◽  
Author(s):  
Walter A Korfmacher ◽  
Kathleen A Cox ◽  
Matthew S Bryant ◽  
John Veals ◽  
Kwokei Ng ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Metabolism ◽  
Discovery Process ◽  
Drug Discovery Process

ReAction!

2009 ◽  
Author(s):  
Mark A. Griep ◽  
Marjorie L. Mikasen
Keyword(s):  
Drug Discovery ◽  
Chemical Weapons ◽  
Trial And Error ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Special Effects ◽  
Invisible Man ◽  
Feature Films ◽  
The World ◽  
Chemical Companies

ReAction! gives a scientist's and artist's response to the dark and bright sides of chemistry found in 140 films, most of them contemporary Hollywood feature films but also a few documentaries, shorts, silents, and international films. Even though there are some examples of screen chemistry between the actors and of behind-the-scenes special effects, this book is really about the chemistry when it is part of the narrative. It is about the dualities of Dr. Jekyll vs. inventor chemists, the invisible man vs. forensic chemists, chemical weapons vs. classroom chemistry, chemical companies that knowingly pollute the environment vs. altruistic research chemists trying to make the world a better place to live, and, finally, about people who choose to experiment with mind-altering drugs vs. the drug discovery process. Little did Jekyll know when he brought the Hyde formula to his lips that his personality split would provide the central metaphor that would come to describe chemistry in the movies. This book explores the two movie faces of this supposedly neutral science. Watching films with chemical eyes, Dr. Jekyll is recast as a chemist engaged in psychopharmaceutical research but who becomes addicted to his own formula. He is balanced by the often wacky inventor chemists who make their discoveries by trial-and-error.

scholarly journals Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 546
Author(s):  
Miroslava Nedyalkova ◽  
Vasil Simeonov
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
De Novo ◽  
Target Prediction ◽  
Natural Compounds ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Unique Source ◽  
The Times ◽  
Partitioning Pattern

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

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