Towards a more general drug target interaction prediction model using transfer learning

Suhartono, Derwin; Majiid, Muhammad Rizki Nur; Handoyo, Alif Tri; Wicaksono, Pandu; Lucky, Henry

doi:10.1016/j.procs.2022.12.148

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Deep learning is currently a highly popular technique with extensive applications and significant value in the biomedical industry. Its remarkable success in computer vision, speech recognition, and natural language processing (NLP) has led to its widespread adoption in DTI and other predictive tasks [ 2 ]. Use deep learning to interpret information about proteome sequences [ 3 ], and use deep learning models to predict antigenic peptides [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning

Wei,

Zhao,

Zhang

et al. 2024

BMC Bioinformatics

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Background CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. Results We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. Conclusions Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS. The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html.

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Section: Introductionmentioning

confidence: 99%

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning

Wei,

Zhao,

Zhang

et al. 2024

BMC Bioinformatics

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“…By leveraging pretrained representations, researchers can significantly enhance the generalization capabilities of DTI models. This transfer learning paradigm has shown promise in improving the accuracy of predictions, especially for rare or poorly characterized drug-target interactions [40, 41, 41].…”

Section: Introductionmentioning

confidence: 99%

Drug-Target-Interaction Prediction with Contrastive and Siamese Transformers

Ikechukwu,

Kumar

2023

Preprint

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As machine learning (ML) becomes increasingly integrated into the drug development process, accurately predicting Drug-Target Interactions (DTI) becomes a necessity for pharmaceutical research. This prediction plays a crucial role in various aspects of drug development, including virtual screening, repurposing of drugs, and proactively identifying potential side effects. While Deep Learning has made significant progress in enhancing DTI prediction, challenges related to interpretability and consistent performance persist in the field. This study introduces two innovative methodologies that combine Generative Pretraining and Contrastive Learning to specialize Transformers for bio-chemical modeling. These systems are designed to best incorporate cross-attention, which enables a nuanced alignment of multi-representation embeddings. Our empirical evaluation will showcase the effectiveness and interpretability of this proposed framework. Through a series of experiments, we provide compelling evidence of its superior predictive accuracy and enhanced interpretability. The primary objective of this research is not only to contribute to the advancement of novel DTI prediction methods but also to promote greater transparency and reliability within the drug discovery pipeline.

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G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Qiu,

Wang,

Tan

et al. 2024

Computers in Biology and Medicine

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Towards a more general drug target interaction prediction model using transfer learning

Cited by 5 publications

References 20 publications

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning

Drug-Target-Interaction Prediction with Contrastive and Siamese Transformers

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

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