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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 117
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| Paper Title: | Explainable Graph Neural Networks (XGNNs) For Protein-Ligand Interaction Interpretation |
| Authors Name: | Subhasankar Khilar , Dr. Elamathi Natarajan |
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| Author Reg. ID: |
IJRTI_209224
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| Published Paper Id: | IJRTI2601071 |
| Published In: | Volume 11 Issue 1, January-2026 |
| DOI: | |
| Abstract: | In modern drug discovery to predicting Protein-Ligand interactions by Graph Neural Network has revolved from the combination of Artificial Intelligence and Structural bioinformatics. As it limited in interpretations, that causes a solid barrier in biomedical research, but in determining and identifying the binding affinity and active compound has achieved [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] a good accuracy by GNN models. Here this review is on interpretation of interactions between proteins and ligands at molecular level, which is an arising field of Explainable Graph Neural Networks (XGNNs). To make robust and inaccurate predictions on binding, the modern study uses such as attention mechanisms, visualization techniques and feature attribution techniques by model. That focuses on given frameworks that identify important ligand atoms and binding residues and physiochemical factors that affect affinity with chemical thought processes. In this review, the challenges of developing biological significant explanation, the transparency along with corollary of dataset biases on interpretability were investigated. Here, the review paper investigated detailed about combination of protein language models to form more reliable, possible paths for further research, interpreting hybrid architectures, transparent, energy sensible GNNs, and drug discovering AI models with scientifically. Here my review, XGNNs reveals the link between the Deep Learning (DL) and Biochemical expertise with confidence of people that will improve both exactness of predictive models and computational models. |
| Keywords: | Graph Neural Networks (GNNs); Explainable AI (XAI); Protein–Ligand Interaction (PLI); Binding Affinity Prediction; Deep Learning; Drug Discovery; |
| Cite Article: | "Explainable Graph Neural Networks (XGNNs) For Protein-Ligand Interaction Interpretation", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a492-a501, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601071.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2601071
Registration ID:209224
Published In: Volume 11 Issue 1, January-2026
DOI (Digital Object Identifier):
Page No: a492-a501 Country: Bengaluru, Karanataka, India Research Area: Life Sciences Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601071 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601071 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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