A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused 
by premature termination codon (PTC) mutations in the dystrophin gene, leading 
to the production of a truncated, non-functional dystrophin polypeptide. 
PTC-suppressing compounds (PTCSC) have been developed in order to restore 
protein translation by allowing the incorporation of an amino acid in place of a 
stop codon. However, limitations exist in terms of efficacy and toxicity. To 
identify new compounds that have PTC-suppressing ability, we selected and 
clustered existing PTCSC, allowing for the construction of a common 
pharmacophore model. Machine learning (ML) and deep learning (DL) models were 
developed for prediction of new PTCSC based on known compounds. We conducted a 
search of the NCI compounds database using the pharmacophore-based model and a 
search of the DrugBank database using pharmacophore-based, ML and DL models. 
Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, 
and DL searches. Our results suggest notable correspondence of the 
pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing 
compounds.