Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

Abstract

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.

Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning

“…Artificial intelligence (AI) applications are an area of intense medical research, particularly to improve diagnosis and for home monitoring of disease severity 9 . However, to our knowledge AI applications are relatively scarce in the field of neuromuscular disorders so far and appear to be restricted to muscle imaging and gene profiling 10–14 . Assessment of facial weakness has thus far only been carried out in facial palsies and almost exclusively on photographs 15–20 .…”

“… 9 However, to our knowledge AI applications are relatively scarce in the field of neuromuscular disorders so far and appear to be restricted to muscle imaging and gene profiling. 10 , 11 , 12 , 13 , 14 Assessment of facial weakness has thus far only been carried out in facial palsies and almost exclusively on photographs. 15 , 16 , 17 , 18 , 19 , 20 As the facial features of MG patients are distinct from healthy subjects, 21 we hypothesized that a short video recording of MG patients contains information that can be used both for diagnostic and monitoring purposes.…”