Drugs are usually small
organic molecules that bind to disease-causing proteins in order to neutralize their
harmful effects. These proteins can be described at the atomic level, and the
resulting information can be used to generate mathematical models able to guide
the design of better drug candidates.
Until recently,
these models employed classical linear regression, which limited their
accuracy. As the sizes of relevant datasets grow, algorithms able to learn from
them are achieving models of increased accuracy and broader applicability. Devising
and applying such algorithms is the object of a research area known as “machine
learning” (ML), the most successful subdivision of artificial intelligence.
This approach is particularly timely given the amount of relevant data now
available along with the maturity of ML algorithms.
In a recent publication in WIREs Computational Molecular Science, Hongjian Li, Kam-Heung Sze, and Gang Lu from the Chinese University of Hong Kong, in collaboration with Pedro Ballester from INSERM in France, review the state of the art in ML research.
The performance
gap between classical and ML-based models, which was already large, has now widened
owing to further methodological improvements. This is also the case for target-specific
models, although there are a few exceptions that might be due to having insufficient
data for that target. Also, against the expectations of
many experts, deep-ML algorithms have not always been more predictive than
those based on more-established ML techniques.
Instead, the
most successful strategies to generate predictive models have been those
identifying better combinations of ML algorithms with numerical descriptions of
the protein–drug interaction. There are also more of these models that are
freely available for others to use, which is important for promoting their
application to real-world problems.
For example, it is often the case that initial drug candidates do not bind sufficiently tightly to the protein and thus are unable to neutralize its effects completely.
A way to increase the potency of a candidate is to make and experimentally test a wide range of its chemical derivatives. Often, the number of derivatives that need to be considered is such that one cannot test all of them experimentally due to time and cost constraints. In this case, a predictive ML model would greatly shorten the time and cost of identifying potent drug candidates by only testing those derivatives predicted to bind most tightly to the protein.
Studies intended to elucidate which type of numerical description works better for a given target are expected to increase in the future. Another probable future trend will be to investigate which targets can be better modelled when their datasets are complemented with datasets from other targets — for example, by exploiting inter-target similarities.
Kindly contributed by the authors
Research article found at: P. Ballester, et al. WIREs Computational and Molecular Science, 2020, doi.org/10.1002/wcms.1465
