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

Molecules

EdenL. Romm¹  IgorF. Tsigelny¹  Kate Wang²  ValentinaL. Kouznetsova³ 

[1] Curematch Inc., 6440 Lusk Blvd, Suite D206, San Diego, CA 92121, USA;MAP program, University of California San Diego (UCSD), La Jolla, CA 92093, USA;San Diego Supercomputer Center, University of California San Diego (UCSD), La Jolla, CA 92093, USA;

关键词: Duchenne muscular dystrophy;    stop codon;    machine learning;    deep learning;    pharmacophore;    PTC-suppressing compounds;   

DOI  :  10.3390/molecules25173886

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown