Drug-target Binding Affinity Research Articles

DFDTA-MultiAtt: Multi-attention based deep learning ensemble fusion network for drug target affinity prediction

<p>An essential step in the drug development process is the accurate detection of drug-target interactions (DTI). The importance of binding affinity values in understanding protein-ligand interactions was previously disregarded, and DTI prediction was only seen as a binary classification problem. In this regard, we introduced the DFDTA-MultiAtt model for predicting the drug target binding affinity in two stages using the structural and sequential information. The first step of the first stage involves retrieving features from sequence data using a bi-directional long short term memory (Bi-LSTM) architecture together with a multi-attention module and dilated convolutional neural network (dilated-CNN) architecture, and the second step features are learnt from structure representation once again using a dilated-CNN. To predict the binding affinity, the second stage uses an ensemble learning model. The proposed model also produces findings with a greater overall accuracy when compared to contemporary state-of-the-art methods. The model generates an enormous +0.006 concordance index (CI) score on the Davis dataset and reduces the mean square error (MSE) by 0.174 on the KIBA dataset.</p>

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling.

The accurate identification of drug-target affinity (DTA) is crucial for advancements in drug discovery and development. Many deep learning-based approaches have been devised to predict drug-target binding affinity accurately, exhibiting notable improvements in performance. However, the existing prediction methods often fall short of capturing the global features of proteins. In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming other baseline methods. The evaluation results on the KIBA dataset reveal that our model achieves the lowest mean square error (MSE) of 0.125, representing a 0.6% reduction compared to the lowest-performing baseline method. Furthermore, the incorporation of queries, keys and values produced by the stacked convolutional neural network (CNN) enables our model to better integrate the local and global context of protein representation, leading to further improvements in the accuracy of DTA prediction.

LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach.

Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief. Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing. In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity. Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.

Multidta: drug-target binding affinity prediction via representation learning and graph convolutional neural networks

Multidta: drug-target binding affinity prediction via representation learning and graph convolutional neural networks

Multimodal Drug Target Binding Affinity Prediction using Graph Local Substructure.

Predicting the binding affinity of drug target is essential to reduce drug development costs and cycles. Recently, several deep learning-based methods have been proposed to utilize the structural or sequential information of drugs and targets to predict the drug-target binding affinity (DTA). However, methods that rely solely on sequence features do not consider hydrogen atom data, which may result in information loss. Graph-based methods may contain information that is not directly related to the prediction process. Additionally, the lack of structured division can limit the representation of characteristics. To address these issues, we propose a multimodal DTA prediction model using graph local substructures, called MLSDTA. This model comprehensively integrates the graph and sequence modal information from drugs and targets, achieving multimodal fusion through a cross-attention approach for multimodal features. Additionally, adaptive structure aware pooling is applied to generate graphs containing local substructural information. The model also utilizes the DropNode strategy to enhance the distinctions between different molecules. Experiments on two benchmark datasets have shown that MLSDTA outperforms current state-of-the-art models, demonstrating the feasibility of MLSDTA.

TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction

Background and objectiveRecent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. MethodsTo address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. ResultsExperimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. ConclusionsIn this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.

Drug-target Binding Affinity Prediction by Multi-layer Bidirectional Fusion Molecular Feature

Drug-target Binding Affinity Prediction by Multi-layer Bidirectional Fusion Molecular Feature

Fusion-Based Deep Learning Architecture for Detecting Drug-Target Binding Affinity Using Target and Drug Sequence and Structure.

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug discovery. Many computational approaches have been proposed due to costly and time-consuming of wet laboratory experiments. In the input representation, most methods only focus on the target sequence properties or target structure properties while ignore the overall contribution. Therefore, we develop a novel fusion protocol based on multiscale convolutional neural networks and graph neural networks, named CGraphDTA, to predict drug-target binding affinity using target sequence and structure. Unlike existing methods, CGraphDTA is the first model constructed with target sequence and structure as input. Concretely, the multiscale convolutional neural networks are utilized to extract target and drug presentation from sequence, graph neural networks are employed to extract graph presentation from target and drug molecular structure. We compare CGraphDTA with the state-of-the-art methods, the results show that our model outperforms the current methods on the test sets. Furthermore, we conduct ablation studies, biological interpretation examination and drug selectivity evaluation, all results suggest that CGraphDTA is a useful tool to predict drug-target binding affinity and accelerate drug discovery.

Drug-target Binding Affinity Prediction Based on Three-branched Multiscale Convolutional Neural Networks

Background: New drugs are costly, time-consuming, and often accompanied by safety concerns. With the development of deep learning, computer-aided drug design has become more mainstream, and convolutional neural networks and graph neural networks have been widely used for drug–target affinity (DTA) prediction. Objective: The paper proposes a method of predicting DTA using graph convolutional networks and multiscale convolutional neural networks. Methods: We construct drug molecules into graph representation vectors and learn feature expressions through graph attention networks and graph convolutional networks. A three-branch convolutional neural network learns the local and global features of protein sequences, and the two feature representations are merged into a regression module to predict the DTA. Results: We present a novel model to predict DTA, with a 2.5% improvement in the consistency index and a 21% accuracy improvement in terms of the mean squared error on the Davis dataset compared to DeepDTA. Morever, our method outperformed other mainstream DTA prediction models namely, GANsDTA, WideDTA, GraphDTA and DeepAffinity. Conclusion: The results showed that the use of multiscale convolutional neural networks was better than a single-branched convolutional neural network at capturing protein signatures and the use of graphs to express drug molecules yielded better results.

Predicting drug-target binding affinity with cross-scale graph contrastive learning.

Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.

Bio-inspired algorithm-based hyperparameter tuning for drug-target binding affinity prediction in healthcare

The greatest challenge for healthcare in drug repositioning and discovery is identifying interactions between known drugs and targets. Experimental methods can reveal some drug-target interactions (DTI) but identifying all of them is an expensive and time-consuming endeavor. Machine learning-based algorithms currently cover the DTI prediction problem as a binary classification problem. However, the performance of the DTI prediction is negatively impacted by the lack of experimentally validated negative samples due to an imbalanced class distribution. Hence recasting the DTI prediction task as a regression problem may be one way to solve this problem. This paper proposes a novel convolutional neural network with an attention-based bidirectional long short-term memory (CNN-AttBiLSTM), a new deep-learning hybrid model for predicting drug-target binding affinities. Secondly, it can be arduous and time-intensive to tune the hyperparameters of a CNN-AttBiLSTM hybrid model to augment its performance. To tackle this issue, we suggested a Memetic Particle Swarm Optimization (MPSOA) algorithm, for ascertaining the best settings for the proposed model. According to experimental results, the suggested MPSOA-based CNN- Att-BiLSTM model outperforms baseline techniques with a 0.90 concordance index and 0.228 mean square error in DAVIS dataset, and 0.97 concordance index and 0.010 mean square error in the KIBA dataset.

GTAMP-DTA: Graph transformer combined with attention mechanism for drug-target binding affinity prediction

Drug target affinity prediction (DTA) is critical to the success of drug development. While numerous machine learning methods have been developed for this task, there remains a necessity to further enhance the accuracy and reliability of predictions. Considerable bias in drug target binding prediction may result due to missing structural information or missing information. In addition, current methods focus only on simulating individual non-covalent interactions between drugs and proteins, thereby neglecting the intricate interplay among different drugs and their interactions with proteins. GTAMP-DTA combines special Attention mechanisms, assigning each atom or amino acid an attention vector. Interactions between drug forms and protein forms were considered to capture information about their interactions. And fusion transformer was used to learn protein characterization from raw amino acid sequences, which were then merged with molecular map features extracted from SMILES. A self-supervised pre-trained embedding that uses pre-trained transformers to encode drug and protein attributes is introduced in order to address the lack of labeled data. Experimental results demonstrate that our model outperforms state-of-the-art methods on both the Davis and KIBA datasets. Additionally, the model's performance undergoes evaluation using three distinct pooling layers (max-pooling, mean-pooling, sum-pooling) along with variations of the attention mechanism. GTAMP-DTA shows significant performance improvements compared to other methods.

Multimodal contrastive representation learning for drug-target binding affinity prediction

In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era of big data. Recent years we have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task. However, these models only considered a single modality of drug and target information, and some valuable information was not fully utilized. In fact, the information of different modalities of drug and target can complement each other, and more valuable information can be obtained by fusing the information of different modalities. In this paper, we introduce a multimodal information fusion model for DTA prediction that is called FMDTA, which fully considers drug/target information in both string and graph modalities and balances the feature representations of different modalities by a contrastive learning approach. In addition, we exploited the alignment information of drug atoms and target residues to capture the positional information of string patterns, which can extract more useful feature information in SMILES and target sequences. Experimental results on two benchmark datasets show that FMDTA outperforms the state-of-the-art model, demonstrating the feasibility and excellent feature capture capability of FMDTA. The code of FMDTA and the data are available at: https://github.com/bestdoubleLin/FMDTA.

An In Silico Study for Expanding the Utility of Cannabidiol in Alzheimer's Disease Therapeutic Development.

Cannabidiol (CBD), a major non-psychoactive component of the cannabis plant, has shown therapeutic potential in Alzheimer's disease (AD). In this study, we identified potential CBD targets associated with AD using a drug-target binding affinity prediction model and generated CBD analogs using a genetic algorithm combined with a molecular docking system. As a result, we identified six targets associated with AD: Endothelial NOS (ENOS), Myeloperoxidase (MPO), Apolipoprotein E (APOE), Amyloid-beta precursor protein (APP), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), and Presenilin-1 (PSEN1). Furthermore, we generated CBD analogs for each target that optimize for all desired drug-likeness properties and physicochemical property filters, resulting in improved pIC50 values and docking scores compared to CBD. Molecular dynamics (MD) simulations were applied to analyze each target's CBD and highest-scoring CBD analogs. The MD simulations revealed that the complexes of ENOS, MPO, and ADAM10 with CBD exhibited high conformational stability, and the APP and PSEN1 complexes with CBD analogs demonstrated even higher conformational stability and lower interaction energy compared to APP and PSEN1 complexes with CBD. These findings demonstrated the capable binding of the six identified targets with CBD and the enhanced binding stability achieved with the developed CBD analogs for each target.

TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers

The proper assessment of target-specific compound selectivity is paramount in the drug discovery context, promoting the identification of drug-target interactions (DTIs) and the discovery of potential leads. On that account, the accurate prediction of an unbiased drug-target binding affinity (DTA) metric is pivotal to understanding the binding process. Most in silico computational approaches, however, neglect the inter-dependency of the proteomics, chemical, and pharmacological spaces and the explainability during the model construction. Furthermore, these methods have yet to actively include information associated with binding pockets during the learning process, which is essential to DTA prediction performance and model explainability. In this study, we propose an end-to-end binding-region-guided Transformer-based architecture that simultaneously predicts the 1D binding pocket and the binding affinity of DTI pairs, where the prediction of the 1D binding pocket guides and conditions the prediction of DTA. This architecture uses 1D raw sequential and structural data to represent the proteins and compounds, respectively, and combines multiple Transformer-Encoder blocks to capture and learn the proteomics, chemical, and pharmacological contexts. The predicted 1D binding pocket conditions the attention mechanism of the Transformer-Encoder used to learn the pharmacological space in order to model the inter-dependency amongst binding-related positions. The results show that the proposed architecture, TAG-DTA, achieved the best performance in DTA prediction compared to state-of-the-art benchmarks, including in unknown subsets of the proteomics and chemical representation spaces. Moreover, the 1D binding pocket prediction increases the discriminative power and robustness of the aggregate representation of the pharmacological space and improves the DTA prediction performance. Overall, this research study validates the applicability of an end-to-end Transformer-based architecture in the context of drug discovery, and that combining computationally different yet contextually related tasks is critical to new findings in the DTI domain. Additionally, it shows that TAG-DTA is capable of providing increasing DTI and prediction understanding due to the nature of the attention blocks and prediction of the 1D binding pocket. The data and source code used in this study are available at: https://github.com/larngroup/TAG-DTA.

Target Reserve and Turnover Parameters Determine Rightward Shift of Enalaprilat Potency From its Binding Affinity to the Angiotensin Converting Enzyme

Drug effects are often assumed to be directly proportional to the fraction of occupied targets. However, for a number of antagonists that exhibit target-mediated drug disposition (TMDD), such as angiotensin-converting enzyme (ACE) inhibitors, drug binding to the target at low concentrations may be significant enough to influence pharmacokinetics but insufficient to elicit a drug response (i.e., differences in drug-target binding affinity and potency). In this study, a pharmacokinetic/pharmacodynamic model for enalaprilat was developed in humans to provide a theoretical framework for assessing the relationship between ex vivo drug potency (IC50) and in vivo target-binding affinity (KD). The model includes competitive binding of angiotensin I and enalaprilat to ACE and accounts for the circulating target pool. Data were obtained from the literature, and model fitting and parameter estimation were conducted using maximum likelihood in ADAPT5. The model adequately characterized time-courses of enalaprilat concentrations and four biomarkers in the renin-angiotensin system and provided estimates for in vivo KD (0.646 nM) and system-specific parameters, such as total target density (32.0 nM) and fraction of circulating target (19.8%), which were in agreement with previous reports. Model simulations were used to predict the concentration-effect curve of enalaprilat, revealing a 6.3-fold increase in IC50 from KD. Additional simulations demonstrated that target reserve and degradation parameters are key factors determining the extent of shift of enalaprilat ex vivo potency from its in vivo binding affinity. This may be a common phenomenon for drugs exhibiting TMDD and has implications for translating binding affinity to potency in drug development.

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

BackgroundObtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive.ResultsDynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA.ConclusionsThe experimental results show that DGDTA can predict DTA more accurately than some other methods.

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

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores

Drug-target affinity prediction is a challenging task in drug discovery. The latest computational models have limitations in mining edge information in molecule graphs, accessing to knowledge in pharmacophores, integrating multimodal data of the same biomolecule and realizing effective interactions between two different biomolecules. To solve these problems, we proposed a method called Graph features and Pharmacophores augmented Cross-attention Networks based Drug-Target binding Affinity prediction (GPCNDTA). First, we utilized the GNN module, the linear projection unit and self-attention layer to correspondingly extract features of drugs and proteins. Second, we devised intramolecular and intermolecular cross-attention to respectively fuse and interact features of drugs and proteins. Finally, the linear projection unit was applied to gain final features of drugs and proteins, and the Multi-Layer Perceptron was employed to predict drug-target binding affinity. Three major innovations of GPCNDTA are as follows: (i) developing the residual CensNet and the residual EW-GCN to correspondingly extract features of drug and protein graphs, (ii) regarding pharmacophores as a new type of priors to heighten drug-target affinity prediction performance, and (iii) devising intramolecular and intermolecular cross-attention, in which the intramolecular cross-attention realizes the effective fusion of different modal data related to the same biomolecule, and the intermolecular cross-attention fulfills the information interaction between two different biomolecules in attention space. The test results on five benchmark datasets imply that GPCNDTA achieves the best performance compared with state-of-the-art computational models. Besides, relying on ablation experiments, we proved effectiveness of GNN modules, pharmacophores and two cross-attention strategies in improving the prediction accuracy, stability and reliability of GPCNDA. In case studies, we applied GPCNDTA to predict binding affinities between 3C-like proteinase and 185 drugs, and observed that most binding affinities predicted by GPCNDTA are close to corresponding experimental measurements.

Drug-target binding affinity prediction using message passing neural network and self supervised learning

BackgroundDrug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much knowledge of the biochemical background. However, there are still room for improvement in DTA prediction: (1) only focusing on the information of the atom leads to an incomplete representation of the molecular graph; (2) the self-supervised learning method could be introduced for protein representation.ResultsIn this paper, a DTA prediction model using the deep learning method is proposed, which uses an undirected-CMPNN for molecular embedding and combines CPCProt and MLM models for protein embedding. An attention mechanism is introduced to discover the important part of the protein sequence. The proposed method is evaluated on the datasets Ki and Davis, and the model outperformed other deep learning methods.ConclusionsThe proposed model improves the performance of the DTA prediction, which provides a novel strategy for deep learning-based virtual screening methods.