Biology subjects

Hooshmand, M.

Publications and source records attributed to Hooshmand, M..

3 recordsLinked to original sources

bioRxiv2025

A Graph-Attention-Based Deep Learning Network for Predicting Biotech-Small-Molecule Drug Interactions

The increasing demand for effective drug combinations has made drug-drug interaction (DDI) prediction a critical task in modern pharmacology. While most existing research focuses on small-molecule drugs, the role of biotech drugs in complex disease treatments remains relatively unexplored. Biotech drugs, derived from biological sources, have unique molecular structures that differ significantly from those of small molecules, making their interactions more challenging to predict. This study introduces BSI-Net, a novel graph attention network-based deep learning framework that improves interaction prediction between biotech and small-molecule drugs. Experimental results demonstrate that BSI-Net outperforms existing methods in multi-class DDI prediction, achieving superior performance across various evaluation types, including micro, macro, and weighted assessments. These findings highlight the potential of deep learning and graph-based models in uncovering novel interactions between biotech and small-molecule drugs, paving the way for more effective combination therapies in drug discovery.

bioinformatics↗

bioRxiv2025

Attention-Based Solution for Synergistic Virus Combination Therapy

Computational drug repurposing is vital in drug discovery research because it significantly reduces both the cost and time involved in the drug development process. Additionally, combination therapy--using more than one drug for treatment--can enhance efficacy and minimize the side effects associated with individual drugs. However, there is currently limited research focused on computational approaches to combination therapy for viral diseases. This paper proposes AI-based models to predict novel drug combinations that can synergistically treat viral diseases. To achieve this, we have compiled a comprehensive dataset containing information on viruses, drug compounds, and their approved interactions. We introduce two attention-based models and compare their performance with traditional machine learning and deep learning models in predicting synergistic drug pairs for treating viral diseases. Among all the methods tested, the random forest algorithm and one of the attention-based models utilizing a customized dot product as a predictor showed the highest performance. Notably, two predicted combinations--acyclovir + ribavirin and acyclovir + Pranobex Inosine--have been experimentally validated to produce a synergistic antiviral effect against the herpes simplex virus type 1, as reported in existing literature.

bioinformatics↗

bioRxiv2024

Antivirals for Monkeypox Virus: Proposing an Effective Machine/Deep Learning Framework

Monkeypox is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle Monkeypox. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the monkeypox virus. This paper proposes a computational framework for monkeypox antiviral prediction. To do this, we have geenrated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of monkeypox antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for monkeypox treatment.

bioinformatics↗