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February 7, 2022 no comments

Re-Imagining Med Chem Strategies: the Tyranny of the n+1 Compound

Finding small molecule drugs is much harder than finding a needle in a haystack – discovering the right arrangement of atoms to bind precisely to a protein target to elicit a particular response is a problem of vast dimensionality.

We are most familiar with the numbers involved when dealing with antibodies: a typical antibody library might contain 1013 different clones – but even that hardly scratches the surface of the 1080 or so possible CDR sequences.  Choosing the sequences to populate a library is critical – and even today new innovations, such as the RxBiologics’ Galaxy™ platform, are dramatically improving the output.

Yet the situation with small molecules is even worse.  Chemists look on jealously at a universe of a mere 1080 possibilities for antibody CDRs!

So DrugBaron asked Nigel Ramsden, who heads up the RxChemistry team, whether the solution lies in bigger libraries and yet higher-throughput screening?

“In a word, no!  Bigger won’t cut it – even with a 10-fold increase in library size, you are still testing only the most miniscule fraction of all possible compounds.  We have to face facts – its NEVER going to be practical to test a material fraction of the universe of possibilities when it comes to small molecules

So the answer has to lie in quality rather than quantity.  Which molecules to test, not how many.

In exactly, the same way Galaxy™ advanced antibody discovery by being smart about which clones to include in the starting library, we have to be smarter in choosing the molecules we test.”

So scaling up real, wet experimentation cannot improve efficiency in small molecule discovery.  But what about in silico searches – are they the answer to the fundamentaly dimensionality problem?  David Fox, Associate Professor of Chemistry at Warwick University and head of the RxChemistry medicinal chemistry team, thinks its only a small part of the solution:

“We can scale in silico searches well beyond the practical limits of any physical high throughput screen.  But even processor time is not infinite and free, so searching ‘everything’ remains well outside the realm of possibilities

And on top of that, in silico searches (like physical screening) are imperfect filters – there are lots of false positives.  So you would need to make many of the in silico hits to find one with the desired profile in a real experiment.  Given that, there is no point including compounds in in silico libraries that are difficult or impossible to make

Instead, we need to increase the diversity of the compounds we do screen (whether in silico or for real).  Right now, both virtual and real libraries are dominated by particular …


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