SVDTI: Stacked variational autoencoder with SMILES-based drug representations for identifying drug-target interaction
The rapid identification of novel drug–target interactions (DTIs) remains a critical challenge in drug development, as traditional experimental methods are both resource-intensive and time-consuming. Motivated by the need to accelerate drug discovery and reduce experimental costs, computational stra...
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| Published in: | Neurocomputing (Amsterdam) Vol. 661; p. 131837 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
14.01.2026
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| ISSN: | 0925-2312 |
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| Abstract | The rapid identification of novel drug–target interactions (DTIs) remains a critical challenge in drug development, as traditional experimental methods are both resource-intensive and time-consuming. Motivated by the need to accelerate drug discovery and reduce experimental costs, computational strategies have emerged as powerful alternatives, leveraging advanced algorithms and data-driven approaches to predict potential DTIs efficiently. In this paper, we introduce a novel method, which employs a stacked variational autoencoder (SVAE) to efficiently predict drug–target interactions, with the goal of enhancing the understanding and identification of these crucial relationships in drug discovery. This model leverages protein sequences and drug chemical properties as input features. It employs a stacked variational autoencoder (SVAE) with Long Short-Term Memory (LSTM) networks to map high-dimensional data into compact, informative low-dimensional vectors. The LSTM architecture captures temporal dependencies in protein sequences, thereby enhancing the model's ability to encode complex patterns. Next, the feature representation is fed into a neural collaborative filtering (NCF) model. This model combines the linear characteristics of matrix factorization with the nonlinear representation power of a multi-layer perceptron (MLP) to generate the final prediction, thereby improving the accuracy of DTI prediction. As a result, in comparison to existing state-of-the-art methods for DTIs prediction, our model demonstrates remarkable improvements in predictive performance. These findings highlight the capability of the proposed model to effectively integrate diverse sources of information for predicting DTIs, addressing critical challenges in drug discovery and offering a robust and efficient framework that contributes valuable perspectives to the field. |
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| AbstractList | The rapid identification of novel drug–target interactions (DTIs) remains a critical challenge in drug development, as traditional experimental methods are both resource-intensive and time-consuming. Motivated by the need to accelerate drug discovery and reduce experimental costs, computational strategies have emerged as powerful alternatives, leveraging advanced algorithms and data-driven approaches to predict potential DTIs efficiently. In this paper, we introduce a novel method, which employs a stacked variational autoencoder (SVAE) to efficiently predict drug–target interactions, with the goal of enhancing the understanding and identification of these crucial relationships in drug discovery. This model leverages protein sequences and drug chemical properties as input features. It employs a stacked variational autoencoder (SVAE) with Long Short-Term Memory (LSTM) networks to map high-dimensional data into compact, informative low-dimensional vectors. The LSTM architecture captures temporal dependencies in protein sequences, thereby enhancing the model's ability to encode complex patterns. Next, the feature representation is fed into a neural collaborative filtering (NCF) model. This model combines the linear characteristics of matrix factorization with the nonlinear representation power of a multi-layer perceptron (MLP) to generate the final prediction, thereby improving the accuracy of DTI prediction. As a result, in comparison to existing state-of-the-art methods for DTIs prediction, our model demonstrates remarkable improvements in predictive performance. These findings highlight the capability of the proposed model to effectively integrate diverse sources of information for predicting DTIs, addressing critical challenges in drug discovery and offering a robust and efficient framework that contributes valuable perspectives to the field. |
| ArticleNumber | 131837 |
| Author | Ha, Jihwan |
| Author_xml | – sequence: 1 givenname: Jihwan surname: Ha fullname: Ha, Jihwan email: jhha@pknu.ac.kr organization: Major of Big Data Convergence, Division of Data Information Science, Pukyong National University, Busan 48513, South Korea |
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| Keywords | Drug Target Neural collaborative filtering Stacked variational autoencoder Drug-target interaction Protein |
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