TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection

Liyaqat, Tanya; Ahmad, Tanvir; Saxena, Chandni

doi:10.1007/s10822-023-00533-1

J Comput Aided Mol Des

2023

DOI: 10.1007/s10822-023-00533-1

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TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection

Tanya Liyaqat,

Tanvir Ahmad,

Chandni Saxena

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2024

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Cited by 3 publications

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Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug–Target Interaction Prediction

Lee,

Kim,

Jun

et al. 2024

J. Chem. Inf. Model.

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Predicting drug−target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of the intricate and exceptionally lengthy protein target sequences. This study introduces MMF-DTI, a lightweight model that uses multimodal fusion, to improve the generalizability of DTI predictions without sacrificing computational efficiency. The MMF-DTI model combines four distinct modalities: molecular sequence, molecular properties, target sequence, and target function description. This approach is noteworthy because it is the first to use natural language-based target function descriptions in predicting DTIs. The effectiveness of MMF-DTI has been confirmed through benchmark data sets, demonstrating its comparable performance in terms of generalizability, especially in scenarios with limited information about the drug or target. Remarkably, MMF-DTI accomplishes this using only half of the parameters and 17% of the VRAM compared with previous state-of-the-art models. This allows it to function even in constrained computational environments. The combination of performance and efficiency highlights the potential of multimodal data fusion in improving the overall applicability of models, providing promising opportunities for future drug discovery endeavors.

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Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug–Target Interaction Prediction

Lee,

Kim,

Jun

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

Graph neural pre-training based drug-target affinity prediction

Ye,

Sun

2024

Front. Genet.

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Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data. However, given the scarcity of drug-target interaction data, pre-training models can only be trained separately on drug and target data, resulting in features that are insufficient for drug-target affinity prediction. To address this issue, in this paper, we design a graph neural pre-training-based drug-target affinity prediction method (GNPDTA). This approach comprises three stages. In the first stage, two pre-training models are utilized to extract low-level features from drug atom graphs and target residue graphs, leveraging a large number of unlabeled training samples. In the second stage, two 2D convolutional neural networks are employed to combine the extracted drug atom features and target residue features into high-level representations of drugs and targets. Finally, in the third stage, a predictor is used to predict the drug-target affinity. This approach fully utilizes both unlabeled and labeled training samples, enhancing the effectiveness of pre-training models for drug-target affinity prediction. In our experiments, GNPDTA outperforms other deep learning methods, validating the efficacy of our approach.

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Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

Qi,

Zhao,

et al. 2024

Molecules

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Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

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TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection

Cited by 3 publications

References 57 publications

Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug–Target Interaction Prediction

Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug–Target Interaction Prediction

Graph neural pre-training based drug-target affinity prediction

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

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