Improved drug-target interaction prediction with intermolecular graph transformer

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment

There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the rapid identification of candidate inhibitors and facilitates the optimization of on-target potency. In this work, we present T-ALPHA, a novel deep learning model that enhances protein-ligand binding affinity prediction by integrating multimodal feature representations within a hierarchical transformer framework to capture information critical to accurately predicting binding affinity. T-ALPHA outperforms all existing models reported in the literature on multiple benchmarks designed to evaluate protein-ligand binding affinity scoring functions. Remarkably, T-ALPHA maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, a powerful capability in real-world drug discovery applications where experimentally determined structures are often unavailable or incomplete. Additionally, we present an uncertainty-aware self-learning method for protein-specific alignment that does not require additional experimental data and demonstrate that it improves T-ALPHA's ability to rank compounds by binding affinity to biologically significant targets such as the SARS-CoV-2 main protease and the epidermal growth factor receptor. To facilitate implementation of T-ALPHA and reproducibility of all results presented in this paper, we made all of our software available at https://github.com/gregory-kyro/T-ALPHA.

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism

Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement. To address the above two problems, in this study, we develop a novel end-to-end framework named DO-GMA for potential DTI identification by incorporating Depthwise Overparameterized convolutional neural network and the Gated Multihead Attention mechanism with shared-learned queries and bilinear model concatenation. DO-GMA first designs a depthwise overparameterized convolutional neural network to learn drug representations from their SMILES strings and protein representations from their amino acid sequences. Next, it extracts drug representations from their 2D molecular graphs through a graph convolutional network. Subsequently, it fuses drug and protein features by combining the gated attention mechanism and the multihead attention mechanism with shared-learned queries and bilinear model concatenation. Finally, it takes the fused drug-target features as inputs and builds a multilayer perceptron to classify unlabeled drug-target pairs (DTPs). DO-GMA was benchmarked against six newest DTI prediction methods (CPI-GNN, BACPI, CPGL, DrugBAN, BINDTI, and FOTF-CPI) under four different experimental settings on four DTI data sets (i.e., DrugBank, BioSNAP, C.elegans, and BindingDB). The results show that DO-GMA significantly outperformed the above six methods based on AUC, AUPR, accuracy, F1-score, and MCC. An ablation study, robust statistical analysis, sensitivity analysis of parameters, visualization of the fused features, computational cost analysis, and case analysis further validated the powerful DTI identification performance of DO-GMA. In addition, DO-GMA predicted that two drug-protein pairs (i.e., DB00568 and P06276, and DB09118 and Q9UQD0) could be interacting. DO-GMA is freely available at https://github.com/plhhnu/DO-GMA.

NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism

Existing deep learning methods have shown outstanding performance in predicting drug-target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug-target interaction pairs. In this study, we propose a novel drug-target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN's capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model's ability to learn local crucial interaction sites in the drug-target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations.

Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration

Identifying the associations between long noncoding RNAs (lncRNAs) and disease is critical for disease prevention, diagnosis and treatment. However, conducting wet experiments to discover these associations is time-consuming and costly. Therefore, computational modeling for predicting lncRNA-disease associations (LDAs) has become an important alternative. To enhance the accuracy of LDAs prediction and alleviate the issue of node feature oversmoothing when exploring the potential features of nodes using graph neural networks, we introduce DPFELDA, a dual-path feature extraction network that leverages the integration of information from multiple sources to predict LDA. Initially, we establish a dual-view structure of lncRNAs and disease and a heterogeneous network of lncRNA-disease-microRNA (miRNA) interactions. Subsequently, features are extracted using a dual-path feature extraction network. In particular, we employ a combination of a graph convolutional network, a convolutional block attention module, and a node aggregation layer to perform multilayer topology feature extraction for the dual-view structure of lncRNAs and diseases. Additionally, we utilize a Transformer model to construct the node topology feature residual network for obtaining node-specific features in heterogeneous networks. Finally, XGBoost is employed for LDA prediction. The experimental results demonstrate that DPFELDA outperforms the benchmark model on various benchmark data sets. In the course of model exploration, it becomes evident that DPFELDA successfully alleviates the issue of node feature oversmoothing induced by graph-based learning. Ablation experiments confirm the effectiveness of the innovative module, and a case study substantiates the accuracy of DPFELDA model in predicting novel LDAs for characteristic diseases.

A review of deep learning methods for ligand based drug virtual screening

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

Revisiting drug-protein interaction prediction: a novel global-local perspective

MOTIVATION

Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance.

RESULTS

We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research.

AVAILABILITY AND IMPLEMENTATION

Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions

Motivation

Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information.

Results

In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life.

Availability and implementation

The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.