GramSeq-DTA: A grammar-based drug-target affinity prediction approach fusing gene expression information

TL;DR

GramSeq-DTA is proposed, which integrates chemical perturbation information with the structural information of drugs and targets and outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets.

Abstract

Drug-target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug-target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The chemical perturbation data is obtained from the L1000 project, which provides information on the upregulation and downregulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction.

Figures (3)

• Figure 1: Preparation of the gene expression dataset. Gene expression information analyzed on 978 landmark genes for the selected drugs is extracted from the L1000 chemical perturbation data. After considering all the biological replicates of the perturbation analysis, a gene regulation matrix is created for both upregulated and downregulated genes.
• Figure 2: Network architecture of the proposed model. The encoded drug information is passed through an GVAE layer, the RNA-Seq information is passed through an FCNN, while the encoded protein information is passed through a series of LSTM layers and 1D CNN layers. Learned representations are concatenated and passed through a FCNN acting as a regression head to predict the affinity.
• Figure 3: Encoding of drug SMILES structures. A parse tree is constructed based on the structural components of SMILES representations. Grammar rules are extracted from the parsed trees. SMILES representations are then converted into one-hot vectors. Finally, the one-hot vectors are transformed into corresponding latent space representations using an encoder network.