2023年11月4日 · Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that …

2024年5月17日 · Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that …

2023年10月25日 · We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our information-rich representations for both proteins and drugs offer a detailed perspective on their interaction.

Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet chal-lenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction.

The identification of drug-target interaction (DTI) is significant in drug discovery and development, which is usually of high cost in time and money due to large amount of molecule and protein space. The application of deep learning in predicting DTI pairs can overcome these limitations through feature engineering.

Introducing DeepDrugDomain: A cutting-edge toolkit for Drug-Target Interaction & Affinity Prediction. Streamlined preprocessing, advanced modeling capabilities, and more - all in one comprehensive library. Revolutionize your DTI and DTA research with #DeepDrugDomain! Check it out now: Github #Bioinformatics #MachineLearning #DrugDiscovery

2024年5月17日 · We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug...

2024年1月1日 · 本研究提出了一种名为FragXsiteDTI的新型Transformer模型，旨在解决DTI预测中的模型解释性和性能优化问题。FragXsiteDTI首次同时利用药物分子片段和蛋白质口袋，通过丰富的信息表示，为药物与蛋白质的相互作用提供了详细的视角。

To address these issues, we propose an end-to-end predicting framework for drug-target interaction named BCM-DTI that takes diverse fragment types into account, including branch chain, common substructure and motif/fragments, and applies a feature learning module based on CNN to learn the synergistic effect between these fragments.

Experiments on three well-known benchmark datasets show that our proposed FusionDTI model achieves the best performance in DTI prediction compared with seven existing state-of-the-art baselines. Furthermore, our case study indicates that FusionDTI could highlight the potential binding sites, enhancing the explainability of the DTI prediction.