PyRMD: A New Fully Automated AI-Powered Ligand-Based Virtual Screening Tool

Artificial intelligence
(AI) algorithms are dramatically redefining
the current drug discovery landscape by boosting the efficiency of
its various steps. Still, their implementation often requires a certain
level of expertise in AI paradigms and coding. This often prevents
the use of these powerful methodologies by non-expert users involved
in the design of new biologically active compounds. Here, the random
matrix discriminant (RMD) algorithm, a high-performance AI method
specifically tailored for the identification of new ligands, was implemented
in a new fully automated tool, PyRMD. This ligand-based virtual screening
tool can be trained using target bioactivity data directly downloaded
from the ChEMBL repository without manual intervention. The software
automatically splits the available training compounds into active
and inactive sets and learns the distinctive chemical features responsible
for the compounds’ activity/inactivity. PyRMD was designed
to easily screen millions of compounds in hours through an automated
workflow and intuitive input files, allowing fine tuning of each parameter
of the calculation. Additionally, PyRMD features a wealth of benchmark
metrics, to accurately probe the model performance, which were used
here to gauge its predictive potential and limitations. PyRMD is freely
available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.