APR May/June 2023 - 24

» DRUG DISCOVERY
»
open the door to exploring the uncharted chemical ocean. When
promising new molecules are identified via physics modeling, they can
be synthesized and put through extensive biological characterization.
AI trained on data from these novel molecules can then be used for
further optimization of drug candidates. Such synergistic integration
of physics advances, and purpose-built AI tools working on new data,
can deliver multiple chemically distinct candidates with uniquely
desirable therapeutic profiles for every program. Finally, these
candidates can be advanced through AI-driven adaptive trials that
take personalization of medicines to a new level.
Given the recent interest in potential applicability of AI in almost every
sphere of human activity, some additional words on the utility of AI in
small molecule drug discovery would be warranted. Much has been
made recently of the use of AI in protein target identification. AI can
be helpful in building so-called knowledge graphs and generating
potential hypotheses for new targets in a disease pathway. Such target
hypotheses must then be rigorously validated in follow-up biological
experiments. Trusting the hypotheses, or perhaps the hype, without
thorough in vitro and in vivo validation is a formula for eventual
disappointment in a drug program. While such use of AI in generating
target hypotheses has utility and should be used as an aid in ongoing
disease pathway analysis, we currently live in a world where there is
an enormous number of validated targets for which we can't find any
reasonable drug candidates. Use of AI notwithstanding, biologists
across the world have worked assiduously to find and validate disease
targets, starting from the early days of the genomics revolution. For
most of those targets, we have no viable drugs. This brings us to the
pressing need for a systematic process of exploring new chemical
space for interesting drug candidates. Once a target is chosen, the
physics, computational chemistry and AI driven process described
above can do just that.
The public discourse on the use of AI in medicine also conflates many
other disparate applications of AI and leads to much confusion. Recent
advancement in protein structure prediction is often cited as a major
revolution in drug discovery. For starters, protein structure prediction
is entirely different from the problem of discovering small molecule
drugs. Evolutionary rules and homology models are critical to the
improvements in protein structure prediction. No such aid exists for
the prediction of protein drug interactions. Further, most current drug
programs start with readily available target protein structures from the
Worldwide Protein Data Bank. Even in the case of many GPCR targets,
high quality structures built using homology and NMR data have
been available for quite some time now. The recent improvements
in protein structure prediction help to fill out the small percentage of
cases where good structures were not available yet. The real problem
of drug discovery starts after a target has been chosen.
Some recent discussions lump together the discovery of biologics
and small molecule drugs. Here again the two problems are entirely
different. AI can have much greater utility in the optimization of
biologics on account of the same evolutionary rules.
The start of a small molecule drug discovery campaign must either
rely on HTS and associated data, or highly accurate and extremely
computationally intensive physics simulations. When certain types
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of AI applications are layered within such simulations, they can help
reduce the computing burden of characterizing quantum behavior of
such systems. But here is a very different type of application of AI, one
which falls well outside of how most companies are trying to use AI in
medicine today.
Moving forward through the steps of small molecule drug development,
given the availability of relevant data, as described earlier, AI can be
used for analyzing structure activity relationships and optimizing drug
candidates. Once more, the details matter. In most such situations in
small molecule drug optimization, we operate in a realm of " small "
data, not big data. Applicable descriptors such as small or big depend
on context. Given the number of parameters involved in a typical drug
optimization problem, the amount of available data is often small and
sparse. The traditional tools of big-data driven AI and deep learning do
not work well in this regime. While most current practitioners in the
field simply try to utilize existing software libraries, what are needed
are AI tools purpose built for such small data scenarios. Building such
tools from scratch is important, but falls far beyond the capabilities of
most teams working in this arena.
Perhaps the takeaway message is that one cannot gloss over the
details. Any one tool is just a tool among many, and not a panacea.
Small molecule drug discovery is a complex problem that requires
significant advancements across diverse disciplines with many
different resulting tools that must come together and work in concert.
Thoughtful integration of advances in physics, AI and many other areas
of science and technology can unlock the potential of the uncharted
chemical ocean and allow the discovery of small molecule treatments
of tomorrow. And we've seen the benefits of exploring entirely novel
chemical entities: our new drug candidates display unique therapeutic
profiles and promise to change the standard of care for the diseases
they address. A steady stream of such drugs is needed to give humanity
the healthier future it deserves.
We are now entering an exciting new era in drug discovery and
development with immense implications for human health.
Reference
1.
Journal of Chemoinformatics. CReM: chemically reasonable mutations framework for
structure generation. Available at: https://jcheminf.biomedcentral.com/articles/10.1186/
s13321-020-00431-w. Accessed May 2, 2023.
Author Biography
Adityo Prakash started Verseon to change the way the
world finds new medicines. He enjoys building fundamental
science-based solutions to major business problems that
impact society. Since the company's inception, Adityo has
guided the development of Verseon's drug discovery platform, novel
drug pipeline, and overall business strategy. Previously, he was the CEO
of Pulsent Corporation and is the primary inventor of technology at
the heart of all video streaming today. He is an inventor on 40 patent
families and received his BS in Physics and Mathematics from Caltech.
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00431-w
APR May/June 2023

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