Dutta Lab - Artificial Intelligence

Predict Drug MoA. The identification of protein target and mechanism of disease are fundamentally important in drug discovery. A pipeline for predicting mechanism of action (MoA) for drug molecules based on gene expression and cell viability data is developed and demonstrated on experimental data. A deep learning network learns the known MoAs over thousands of gene expression and cell viability training data and is shown to predict the unknown MoAs over test data with high efficacy.

Abhishek Dutta. Predicting drug mechanics by deep learning on gene and cell activities. In IEEE Engineering in Medicine and Biology Conference, pages 2916–2919, 2022.

Optimal Drug Generation. This research aims at automatic generation of novel small molecules guided by favorable molecular properties. A generative adversarial network is trained on thousands of stable small molecular graphs followed by a graph convolutional network trained to predict solubility and toxicity from molecular descriptors. The generator network generates unique small molecules guided by the graph convolutional network to have desirable drug-like properties.

Yue, R., Dutta, A. Computational Systems Biology in Disease Modeling and Control, Review and Perspectives. Nature Partner Journal Systems Biology and Applications, 8(1), pp 1-16, 2022.

Repurpose Drugs on Graph. Here we present a graph learning based pipeline to identify the potential drugs that are likely to intervene in COVID-19 virus infection using RNA-seq data and molecular interactions and features. We do so by detecting disease-related genes and pathways first, then constructing a heterogeneous graph model that integrates protein-protein interactions, drug-protein interaction and drug-target interactions, and finally applying graph convolutional network on the graph model to predict potential drug molecules.

Yue, R., Dutta, A. Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction. Interdisciplinary Sciences: Computational Life Sciences, vol 17, pp 185-199, 2025.

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