Drug repurposing for obsessive-compulsive disorder using deep learning-based binding affinity prediction models.

Obsessive-compulsive disorder (OCD) is a chronic psychiatric disease in which patients suffer from obsessions compelling them to engage in specific rituals as a temporary measure to alleviate stress. In this study, deep learning-based methods were used to build three models which predict the likelihood of a molecule interacting with three biological targets relevant to OCD, SERT, D2, and NMDA. Then, an ensemble model based on those models was created which underwent external validation on a large drug database using random sampling.

Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype.