APR May/June 2023 - 22

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DRUG DISCOVERY
»
Exploring New Chemical
Space for the Treatments
of Tomorrow
Adityo Prakash, CEO
Verseon International Corporation
Despite all the medical advances we have achieved over the last
hundred years, there are still too many diseases we can't treat or
treat with a long list of undesirable side effects. To state the obvious,
humanity needs to find ways to accelerate the development of new
medicines. And within the field of pharmaceutical medicines, small
molecule drugs have been the mainstay of modern pharmacopeia for
good reason. These low-molecular weight organic compounds can
typically be administered orally and can enter cells to interrupt disease
processes that involve intracellular targets. But systematic design
and development of novel small molecules drugs have proved to be
dauntingly difficult.
High throughput screening (HTS), which is a collection of brute-force
trial-and-error experiments on a pool of pre-synthesized compounds,
continues to be the foundation of current small molecule drugdiscovery-and
fundamentally limits the speed of discovery and
our ability to explore completely new drug-like compounds that the
world needs. The total number of distinct chemotypes in the world's
HTS libraries is less than 10 million. The time and resources required to
synthesize novel chemotypes means that there exists a very practical
synthesis bottleneck limiting any hope of dramatically increasing
the number of distinct molecules across HTS libraries in a reasonable
timeframe. By comparison, the total number of distinct, synthesizable,
drug-like compounds is more than 1033
.1 Many, if not most, drugs
of the future, with highly desirable therapeutic profiles, lie in that
uncharted chemical ocean of possibilities. Current drug discovery
methods are not even exploring a tide-pool by the side of the ocean;
they're perhaps exploring a droplet!
Now many players in the pharmaceutical industry hope to feed
currently available experimental data to AI to solve all problems of
small molecule drug discovery. Unfortunately, using AI in this manner
does nothing to help find completely novel drugs for all the diseases
we cannot adequately treat today. To understand why, requires us to
step back and analyze the problem just a bit more in depth.
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| May/June 2023
APR May/June 2023

Table of Contents for the Digital Edition of APR May/June 2023
