Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation

Cyanobacteria strains have the potential to produce bioactive
compounds
that can be used in therapeutics and bioremediation. Therefore, compiling
all information about these compounds to consider their value as bioresources
for industrial and research applications is essential. In this study,
a searchable, updated, curated, and downloadable database of cyanobacteria
bioactive compounds was designed, along with a machine-learning model
to predict the compounds’ targets of newly discovered molecules.
A Python programming protocol obtained 3431 cyanobacteria bioactive
compounds, 373 unique protein targets, and 3027 molecular descriptors.
PaDEL-descriptor, Mordred, and Drugtax software were used to calculate
the chemical descriptors for each bioactive compound database record.
The biochemical descriptors were then used to determine the most promising
protein targets for human therapeutic approaches and environmental
bioremediation using the best machine learning (ML) model. The creation
of our database, coupled with the integration of computational docking
protocols, represents an innovative approach to understanding the
potential of cyanobacteria bioactive compounds. This resource, adhering
to the findability, accessibility, interoperability, and reuse of
digital assets (FAIR) principles, is an excellent tool for pharmaceutical
and bioremediation researchers. Moreover, its capacity to facilitate
the exploration of specific compounds’ interactions with environmental
pollutants is a significant advancement, aligning with the increasing
reliance on data science and machine learning to address environmental
challenges. This study is a notable step forward in leveraging cyanobacteria
for both therapeutic and ecological sustainability.