Medical Design Briefs - August 2023 - 29

Drug Development
New AI Model Offers Way to Speed Up Drug Discovery
By applying a language model to protein-drug interactions, researchers can quickly
screen large libraries of potential drug compounds.
Researchers can screen more than 100 million compounds in a single day - much more than any existing model. (Credit: iStock)
H
uge libraries of drug compounds may hold potential
treatments for a variety of diseases, such as cancer or
heart disease. Ideally, scientists would like to experimentally
test each of these compounds against all possible
targets but doing that kind of screen is prohibitively time
consuming.
In recent years, researchers have begun using computational
methods to screen those libraries in hopes of speeding up drug
discovery. However, many of those methods also take a long time,
as most of them calculate each target protein's three-dimensional
structure from its amino-acid sequence, then use those structures
to predict which drug molecules it will interact with.
Researchers at MIT and Tufts University have now devised
an alternative computational approach based on a type of artificial
intelligence algorithm known as a large language model.
These models - one well-known example is ChatGPT - can
analyze huge amounts of text and figure out which words (or,
in this case, amino acids) are most likely to appear together.
The new model, known as ConPLex, can match target proteins
with potential drug molecules without having to perform
the computationally intensive step of calculating the molecules'
structures.
Using this method, the researchers can screen more than
100 million compounds in a single day - much more than any
existing model.
Medical Design Briefs, August 2023
" This work addresses the need for efficient and accurate in
silico screening of potential drug candidates, and the scalability
of the model enables large-scale screens for assessing off-target
effects, drug repurposing, and determining the impact of mutations
on drug binding, " says Bonnie Berger, the Simons Professor
of Mathematics, head of the Computation and Biology group
in MIT's Computer Science and Artificial Intelligence Laboratory
(CSAIL), and one of the senior authors of the new study.
Lenore Cowen, a professor of computer science at Tufts University,
is also a senior author of the paper, which appears in the
Proceedings of the National Academy of Sciences.1
Rohit Singh, a
CSAIL research scientist, and Samuel Sledzieski, an MIT graduate
student, are the lead authors of the paper, and Bryan
Bryson, an associate professor of biological engineering at MIT
and a member of the Ragon Institute of MGH, MIT, and Harvard,
is also an author. In addition to the paper, the researchers
have made their model available for other scientists to use.
Making Predictions
In recent years, computational scientists have made great advances
in developing models that can predict the structures of
proteins based on their amino-acid sequences. However, using
these models to predict how a large library of potential drugs
might interact with a cancerous protein, for example, has proven
challenging, mainly because calculating the three- dimensional
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Medical Design Briefs - August 2023

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