Biology subjects

Yella, J. K.

Publications and source records attributed to Yella, J. K..

3 recordsLinked to original sources

bioRxiv2022

GraMDTA: Multimodal Graph Neural Networks for Predicting Drug-Target Associations

Finding novel drug-target associations is vital for drug discovery. However, screening millions of small molecules for a select target protein is challenging. Several computational approaches have been developed in the past using Machine learning methods for computational drug-target association (DTA) prediction predominantly use structural data of drugs and proteins. Some of these approaches use knowledge graph networks and link prediction. To the best of our knowledge there have been no approaches that use both structural learning that offers molecular-based representations and knowledge graph-based learning which offers interaction-based representations for DTA discovery. Based on the premise that multimodal sources of information acting complimentarily could improve the robustness of DTA predictions, we developed GraMDTA, a multimodal graph neural network that learns both structural and knowledge graph representations utilizing multi-head attention to fuse the multimodal representations. We compare GraMDTA with other computational approaches for DTA prediction to demonstrate the power of multimodal fusion for discovery of DTA.

bioinformatics↗

bioRxiv2020

MGATRx: Discovering Drug Repositioning Candidates Using Multi-view Graph Attention

In-silico drug repositioning or predicting new indications for approved or late-stage clinical trial drugs is a resourceful and time-efficient strategy in drug discovery. However, inferring novel candidate drugs for a disease is challenging, given the heterogeneity and sparseness of the underlying biological entities and their relationships (e.g., disease/drug annotations). By integrating drug-centric and disease-centric annotations as multiviews, we propose a multi-view graph attention network for indication discovery (MGATRx). Unlike most current similarity-based methods, we employ graph attention network on the heterogeneous drug and disease data to learn the representation of nodes and identify associations. MGATRx outperformed four other state-of-art methods used for computational drug repositioning. Further, several of our predicted novel indications are either currently investigated or are supported by literature evidence, demonstrating the overall translational utility of MGATRx. CCS CONCEPTSO_LIApplied computing [->] Biological networks; Bioinformatics C_LIO_LIComputing methodologies [->] Semantic networks C_LI ACM Reference formatJaswanth K Yella, Anil G Jegga 2020. MGATRx: Discovering Drug Repositioning Candidates Using Multi-view Graph. In Proceedings of BIOKDD20: International Workshop on Data Mining for Bioinformatics. San Diego, CA, USA, 9 pages.

bioinformatics↗

bioRxiv2019

Pan-transcriptome-based Candidate Therapeutic Discovery for Idiopathic Pulmonary Fibrosis

BACKGROUNDAlthough the advent of two FDA-approved therapies for idiopathic pulmonary fibrosis (IPF) has energized the field, their effects are largely suppressive than pulmonary fibrosis remission- or reversion-inducing. Hence, the pursuit for newer IPF therapeutics continues. Recent studies show that joint analysis of systems biology level information with drug-disease connectivity are effective in discovery of biologically relevant candidate therapeutics. METHODSPublicly available gene expression signatures from IPF patients are used to query large scale perturbagen signature library to discover compounds that can potentially reverse dysregulated gene expression in IPF. Two methods are used to calculate IPF-compound connectivity: gene expression-based connectivity and feature-based connectivity. Identified compounds are further prioritized based on their shared mechanism(s) of action. RESULTSWe identified 77 compounds as potential candidate therapeutics for IPF. Of these 39 compounds are either FDA-approved for other diseases or are currently in phase 2/3 clinical trials suggesting their repurposing potential for IPF. Among these compounds are multiple receptor kinase inhibitors (e.g., nintedanib, currently approved for IPF, and sunitinib), aurora kinase inhibitor (barasertib), EGFR inhibitors (erlotinib, gefitinib), calcium channel blocker (verapamil), phosphodiesterase inhibitors (roflumilast, sildenafil), PPAR agonists (pioglitazone), HDAC inhibitors (entinostat), and opioid receptor antagonists (nalbuphine). As a proof-of-concept, we performed in vitro validations with verapamil using lung fibroblasts from IPF and show its potential benefits in pulmonary fibrosis. CONCLUSIONSSince about half of the candidates discovered in this study are either FDA-approved or are currently in clinical trials for other diseases, rapid translation of these compounds as potential IPF therapeutics is feasible. Further, the generalizable, integrative connectivity analysis framework in this study can be readily adapted in early phase drug discovery for other common and rare diseases with transcriptomic profiles.

genomics↗