I’ve just been sent details of a new medicinal chemistry course.
This course explores how to bring a drug from concept to market, and how a drug's chemical structure relates to its biological function. The course opens with an introduction to the drug approval process. This introduction combines the social, economic, and ethical aspects of drug discovery. Topics include how diseases are selected for treatment, the role of animal testing, and the costs of various discovery phases. The course then focuses on the scientific side of drug discovery. Topics include how drugs interact with biological molecules, drug absorption and elimination, and the discovery of weakly active molecules and their optimization into viable drugs.
The course starts 10 March, it is estimated the course will require 6-8 hours per week and runs for 7 weeks. The course was organised by Erland Stevens who wrote the medchem textbook Medicinal Chemistry: The Modern Drug Discovery Process.
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I’ve updated the page on Aldehyde Oxidase, an enzyme in metabolism of a wide variety of nitrogen heterocycles.
I’ve also included A recent publication DOI that suggests a simple test for the early identification of heteroaromatic drug candidates that have a high probability of metabolism by AO. Bis(((difluoromethyl)sulfinyl)oxy)zinc (DFMS) was used as a source of the CF2H racial, simple LCMS was then used to identify a characteristic M+50 peak. It is also possible to scale up and isolate these metabolically blocked compounds and retest them for improved qualities.
I’ve just updated the page on HERG activity, I’ve included the results of matched pair analysis conducted on the database of berg activity that I have been compiling.
Whilst much computational work is undertaken to support, library design, virtual screening, hit selection and affinity optimisation the reality is that the most challenging issues to resolve in drug discovery often revolve around absorption, distribution, metabolism and excretion (ADME). Whilst we can measure the levels of parent drug in various medium tracking metabolic fate can often be a considerably more difficult proposition requiring significant resources. For this reason prediction of sites of metabolism has become the subject of current interest.
FAME DOI is a collection of random forest models trained on a comprehensive and highly diverse data set of 20,000 small molecules annotated with their experimentally determined sites of metabolism taken from multiple species (rat, dog and human). In addition dedicated models are available to predict sites of metabolism of phase I and II processes.
FAME offers a high performance prediction of sites of metabolism mediated by a wide variety of mechanisms.
The Drug Discovery Resources section of the website is intended to act as a resource for scientists undertaking drug discovery, it was originally simply a web page version of a course I used to give but has been continuously expanded and updated. Since this takes a far amount of time I like to monitor usage to check that it is being used.
In 2013 the Drug Discovery Resources section was viewed by nearly 24,000 unique visitors (there were 9000 unique visitors in 2012), 27% of which made more than one visit. There were 65,000 page views and on average visitors viewed two pages per visit. There were visitors from 141 different countries with the US and UK being the most common.
The most frequently accessed pages were
The most popular sections were
The top search queries were
Plasma Protein Binding
All the best for Christmas and every success in the New Year,
All monies saved by using an electronic card will be donated to multiple sclerosis research
More details from the announcement
For those of you that are already SureChem users you will be familiar with the functionality and how it works; but for those that weren't SureChEMBL takes feeds of full text patents, identifies chemical objects from either the in-line text or from images and adds 2-D chemical structures. This is then loaded into a database and is searchable by chemical structure, so you can do substructure, similarity searching and so forth - all the good things you'd expect from a chemical database. This chemical search functionality is unavailable from the public, published patent documents, and is really essential for anyone seriously using the patent literature. Oh, and the system does this live, so as patents are published, they are processed and added to the system - the delay between publication and structures being available in SureChEMBL is about a day when converted from text, and a few days when converted from image sources
I’ve just updated the aromatic bioisosteres page to include the bicyclo[1.1.1]pentane replacement for phenyl described in a recent publication DOI.
I’ve continued to collect details of fragment based screening hits that have been reported in the literature. There are now over 600 hits reported for 113 different targets culled from over 160 publications. I’ll update the calculated properties for those compounds in due course. I was interested in seeing if the physicochemical profiles are different depending on the type of target, however as the plot below shows, the majority of those hits have been identified against enzyme targets so I think I’ll need more data before any meaningful conclusions can be made.
In contrast when looking at the screening technology used a variety of technologies have afforded a substantial number of hits, when I’ve abstracted the latest batch of papers I’ll have a look at the profiles of the compounds identified using each technology.
Finding the data is getting more of a challenge, it seems as fragment screening becomes more mainstream it is often not mentioned in the title or abstract. So if you have recently published a relevant paper if you could send me the reference or even a pdf I’d be very grateful.