Machine Learning Uncovers Natural Product Modulators of the 5‑Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms

Machine learning
(ML) models have made inroads into chemical sciences,
with optimization of chemical reactions and prediction of biologically
active molecules being prime examples thereof. These models excel
where physical experiments are expensive or time-consuming, for example,
due to large scales or the need for materials that are difficult to
obtain. Studies of natural products suffer from these issuesthis
class of small molecules is known for its wealth of structural diversity
and wide-ranging biological activities, but their investigation is
hindered by poor synthetic accessibility and lack of scalability.
To facilitate the evaluation of these molecules, we designed ML models
that predict which natural products can interact with a particular
target or a relevant pathway. Here, we focused on discovering natural
products that are capable of modulating the 5-lipoxygenase (5-LO)
pathway that plays key roles in lipid signaling and inflammation.
These computational approaches led to the identification of nine natural
products that either directly inhibit the activity of the 5-LO enzyme
or affect the cellular 5-LO pathway. Further investigation of one
of these molecules, deltonin, led us to discover a new cell-type-selective
mechanism of action. Our ML approach helped deorphanize natural products
as well as shed light on their mechanisms and can be broadly applied
to other use cases in chemical biology.