Corey synthesis code

From the title of this #BucketListPapers you would think it was just out. Given the resurgence of deep learning methods to devises synthetic routes you might expect this, but it was published in 1969; a couple of months after the Moon landings! The work is famous in organic chemistry, and E.J.Corey won a Nobel prize. Although you may have been taught the methods (and read the books) you may not have read the paper ‘Computer-Assisted Design of Complex Organic Synthese’**  The paper is long and describes in detail many of the chem-infomatics issues that come around when writing a piece of software for use by chemists. All through the paper you will keep saying to yourself ‘this was 1969!’. The process of automating the retro-synthetic analysis of an organic molecule is hard. Like a game of chess once a few moves have been made the possible combinations multiply.

One important facet of the design of this software was the continued input of the chemist to ‘guide’ the algorithm through the process – this also saves computational effect and memory. The descriptions of a chemical structure drawing package***, mapping the structure into a graph, and perceiving the chemical functionality are still relevant today, and the methods, for the most part, unchanged. The description of the SSSR (Smallest Set of Smallest Ring problem) is in here too!

We picked out Figure 2, one of many describing the algorithm, because it starts with ‘Chemists enters target molecule’ and ends with ‘Chemist satisfied?’. The essential principles of devising a computer program to design synthetic routes is still current. The approach taken in the program is supervised; the chemical disconnections / forward reactions need to be encoded and as such it is hard to keep it up-to-date and modern.

Computer-Assisted Design of Complex Organic Syntheses

Science 10 Oct 1969: Vol. 166, Issue 3902, pp. 178-192

A mention must be made to the current approaches using Deep Learning to effectively extract the chemical reactions from the data and provide the body of knowledge in an unsupervised and updatable manner.

Planning chemical syntheses with deep neural networks and symbolic AI

Nature 2018, 555(7698), 604-610.

** – of additional note: The modern availability of journals on-line means we miss that trip to library and the discovery of ‘the paper after the one we were looking for’ moment. For this paper (when you download the pdf) just read the start of the following paper….  still relevant today….

*** – and they basically look the same today…

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe