Mittapally Nivesh supervised by Prof. P. Krishna Reddy received his Master of Science by Research in Computer Science Engineering (CSE). Here’s a summary of his research work on Molecular Subgraph Extraction for Explainable Drug-Target Interaction Prediction Using Graph Neural Networks

The identification of Drug-Target Interactions (DTIs) is a critical step in the drug discovery process, involving the assessment of binding affinities between small-molecule drug candidates and biological target proteins. Over the past decade, graph neural network (GNN) based approaches have substantially improved the accuracy of binding affinity prediction by representing drug molecules as molecular graphs and learning chemically informative embeddings through message-passing operations. Despite these advances, a fundamental gap persists in the literature: existing GNN-based DTI models predict whether a drug candidate is likely to bind to a target, but do not identify which subgraph of the drug molecule is structurally responsible for that interaction. From a drug discovery perspective, this subgraph, known as the pharmacophore, is the essential structural and chemical feature that mediates binding, and its identification is indispensable for lead optimisation, scaffold design, and the rationalisation of structureactivity relationships. There is an opportunity to address this gap by exploiting the knowledge encoded in a trained GNN model to identify potential pharmacophores from drug candidates. 

To address this problem, in this thesis we propose ExplainableDeepGNN, a two-phase GNN-based framework in which the Monte Carlo Tree Search (MCTS) algorithm is employed to extract potential pharmacophores from drug candidates by leveraging a trained GNN model. In the first phase, we employ a GNN-based model named GraphNetDTI to compute the binding affinity score for a given drug-target pair, and potential drug candidates are identified based on this score. In the second phase, by employing the MCTS algorithm, the trained GraphNetDTI model is leveraged to extract potential subgraphs representing pharmacophores from the identified drug candidates. 

The framework is evaluated on three benchmark datasets: Davis, Kiba, and Allergy. The experimental results show that GraphNetDTI achieves high predictive accuracy on all three datasets. Furthermore, ExplainableDeepGNN successfully extracts pharmacophores of relatively small size from the identified drug candidates, with the predicted binding affinities of the extracted subgraphs being very similar to those of the corresponding drug candidates. 

The proposed approach helps explore the potential pharmacophore of a given drug candidate, which enhances the explainability of drug-target interaction prediction and provides deeper insights into crucial molecular interactions. 

July 2026