Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4

Bromodomain-containing
protein 4 (BRD4) is implicated in the pathogenesis
of a number of different cancers, inflammatory diseases and heart
failure. Much effort has been dedicated toward discovering novel scaffold
BRD4 inhibitors (BRD4is) with different selectivity profiles and potential
antiresistance properties. Structure-based drug design (SBDD) and
virtual screening (VS) are the most frequently used approaches. Here,
we demonstrate a novel, structure-based VS approach that uses machine-learning
algorithms trained on the priori structure and activity knowledge
to predict the likelihood that a compound is a BRD4i based on its
binding pattern with BRD4. In addition to positive experimental data,
such as X-ray structures of BRD4–ligand complexes and BRD4
inhibitory potencies, negative data such as false positives (FPs)
identified from our earlier ligand screening results were incorporated
into our knowledge base. We used the resulting data to train a machine-learning
model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR
achieved a 20–30% higher AUC-ROC than that of Glide using the
same test set. When conducting in vitro experiments against a library
of previously untested, commercially available organic compounds,
the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover,
inverting the machine-learning model provided easy access to structure–activity
relationship (SAR) interpretation for hit-to-lead optimization.