Predicting drug-target affinity through triple pre-activated random residual planet convolution coupled attention network and contact maps.

MotivationLarge-scale prediction of drug–target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both drugs and proteins. However, sequence-based algorithms ignore the structural information of molecules and proteins, while graph-based algorithms are insufficient in feature extraction and information interaction.ResultsIn this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for interpretable DTA prediction. It can adaptively acquire feature representations of drugs and proteins and allow information to interact at the graph level, effectively combining the advantages of both sequence-based and graph-based approaches. Experimental results have shown that NHGNN-DTA achieved new state-of-the-art performance. It achieved the mean squared error (MSE) of 0.196 on the Davis dataset (below 0.2 for the first time) and 0.124 on the KIBA dataset (3% improvement). Meanwhile, in the case of cold start scenario, NHGNN-DTA proved to be more robust and more effective with unseen inputs than baseline methods. Furthermore, the multi-head self-attention mechanism endows the model with interpretability, providing new exploratory insights for drug discovery. The case study on Omicron variants of SARS-CoV-2 illustrates the efficient utilization of drug repurposing in COVID-19.Availability and implementationThe source code and data are available at https://github.com/hehh77/NHGNN-DTA.

Drug-target affinity (DTA) prediction plays a crucial role in accelerating the drug development process. Although deep learning-based models achieve strong performance in benchmark datasets, their predictive accuracy declines sharply in cold-start scenarios, i.e., when encountering drugs or proteins absent from the training set. This limitation arises from the restricted scale of training datasets, leading to features learned by end-to-end DTA models lacking sufficient generalization. To address this challenge, we propose a novel approach named LLMDTA (Large Language Model for DTA), leveraging the power of biological language models to tackle the cold-start problem in DTA prediction. Specifically, we use Mol2Vec, a molecular pre-training model, and ESM2, a protein language model, as feature extractors. To seamlessly integrate these pre-trained features into downstream DTA prediction, we employ a 1D-CNN-based encoder to extract independent molecular features. In addition, a bilinear attention module is designed to capture interactive molecular features between drugs and proteins. Finally, independent and interactive features are fused to predict binding affinity. Experimental results in three benchmark datasets demonstrate that LLMDTA consistently outperforms state-of-the-art baselines in both warm-start and cold-start scenarios, with notable improvements in novel-protein and novel-pair settings. Furthermore, a case study involving the epidermal growth factor receptor illustrates the ability of LLMDTA to identify novel binding affinities between previously unseen drugs and the target protein, validated through molecular docking. In general, LLMDTA represents a promising and practical tool for advancing DTA prediction in real-world applications. The implemented code and datasets are available online at https://github.com/Chris-Tang6/LLMDTA.

BackgroundThe rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature. Machine learning, particularly deep learning, has significantly improved the efficiency of drug discovery. In the core task of predicting drug–target affinity (DTA), deep learning enhances predictive performance by automatically extracting complex features from compounds and proteins.MethodsTraditional approaches often rely heavily on sequence and two-dimensional structural information, overlooking critical three-dimensional and physicochemical properties. To address this, we propose a novel model—Cross Attention Fusion based on Information Enhancement for Drug–Target Affinity Prediction (CAFIE-DTA)—which incorporates protein 3D curvature and electrostatic potential information. The model approximates protein surface curvature using Delaunay triangulation, calculates total electrostatic potential via Adaptive Poisson-Boltzmann Solver (APBS) software, and employs cross multi-head attention to fuse physicochemical and sequence information of proteins. Simultaneously, it integrates graph-based and physicochemical features of compounds using the same attention mechanism. The resulting protein and compound vectors are concatenated for affinity prediction.ResultsCross-validation and comparative evaluations on the benchmark Davis and KIBA datasets demonstrate that CAFIE-DTA outperforms existing methods. On the Davis dataset, it achieved improvements of 0.003 in confidence interval (CI) and 0.022 in R2. On the KIBA dataset, it improved MSE by 0.008, CI by 0.005, and R2 by 0.017. Compared to traditional models relying on 2D structures and sequence data, CAFIE-DTA shows superior performance in DTA prediction. The source code is available at: https://github.com/NTU-MedAI/CAFIE-DTA.

Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.

Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area. Drug–target affinity (DTA) prediction is the most important step of computer-aided drug design, which could speed up drug development and reduce resource consumption. With the development of deep learning, the introduction of deep learning to DTA prediction and improving the accuracy have become a focus of research. In this paper, utilizing the structural information of molecules and proteins, two graphs of drug molecules and proteins are built up respectively. Graph neural networks are introduced to obtain their representations, and a method called DGraphDTA is proposed for DTA prediction. Specifically, the protein graph is constructed based on the contact map output from the prediction method, which could predict the structural characteristics of the protein according to its sequence. It can be seen from the test of various metrics on benchmark datasets that the method proposed in this paper has strong robustness and generalizability.

Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery. Most computational approaches treat DTI prediction as a binary classification problem, determining whether drug-target pairs interact. However, with the growing availability of drug-target binding affinity data, this binary task can be reframed as a regression problem focused on drug-target affinity (DTA). DTA quantifies the strength of drug-target binding, offering more detailed insights than DTI and serving as a valuable tool for virtual screening in drug discovery. Accurately predicting compound interactions with targets can accelerate the drug development process. In this study, we introduce a deep learning model named TC-DTA for DTA prediction, leveraging convolutional neural networks (CNN) and the encoder module of the transformer architecture. We begin by extracting raw drug SMILES strings and protein amino acid sequences from the dataset, which are then represented using various encoding methods. Subsequently, we employ CNN and the transformer's encoder module to extract features from the drug SMILES strings and protein sequences, respectively. Finally, the feature information is concatenated and input into a multi-layer perceptron to predict binding affinity scores. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, comparing it with methods such as KronRLS, SimBoost, and DeepDTA. Our model, TC-DTA, outperformed these baseline methods based on evaluation metrics like Mean Squared Error (MSE), Concordance Index (CI), and Regression towards the Mean Index ( rm2 ). These results highlight the effectiveness of the Transformer's encoder and CNN in extracting meaningful representations from sequences, thereby enhancing DTA prediction accuracy. This deep learning model can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, machine learning technology offers a more effective and efficient approach to drug discovery.

Drug-Target Affinity (DTA) prediction plays a crucial role in drug discovery, and accurate DTA prediction can significantly reduce the cost of drug development. While most studies focus on the entire protein structure, they often overlook the local structure of protein pockets which play a vital role in DTA due to their direct interaction with drugs. At the methodological level, numerous deep learning approaches have been developed to predict DTA using protein and drug sequences or structures, yet the effective utilization of protein and drug features remains a pressing challenge. Our study proposes leveraging pre-trained models to represent sequence features of protein and drug separately. Subsequently, we construct a geometric graph neural network module capable of parallelizing diverse spatial structural information. We conducted experiments on three public datasets and compared our approach with current state-of-the-art (SOTA) methods, validating the effectiveness of our method. Furthermore, we compared the impact of entire proteins versus protein pockets on DTA, further affirming the reliability of our approach. Consequently, our method (called PGDTA) enhances the accuracy of DTA prediction, thereby aiding in improving the efficiency of the drug discovery process. The source code and data are available online at https://www.github.com/zpliulab/PGDTA.

Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs. Artificial intelligence (AI), especially deep learning models, can automatically extract high-dimensional features from the biological sequences of drug molecules and target proteins. This technology demonstrates lower complexity in DTA prediction compared to traditional experimental methods, particularly when handling large-scale data. In this study, we introduce a multimodal deep neural network model for DTA prediction, referred to as MDNN-DTA. This model employs Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to extract features from the drug and protein sequences, respectively. One notable strength of our method is its ability to accurately predict DTA directly from the sequences of the target proteins, obviating the need for protein 3D structures, which are frequently unavailable in drug discovery. To comprehensively extract features from the protein sequence, we leverage an ESM pre-trained model for extracting biochemical features and design a specific Protein Feature Extraction (PFE) block for capturing both global and local features of the protein sequence. Furthermore, a Protein Feature Fusion (PFF) Block is engineered to augment the integration of multi-scale protein features derived from the abovementioned techniques. We then compare MDNN-DTA with other models on the same dataset, conducting a series of ablation experiments to assess the performance and efficacy of each component. The results highlight the advantages and effectiveness of the MDNN-DTA method.

Background: As the basic material of life, protein regulates physiological activities such as material in and out of cells, signal transduction, metabolism and so on. However, studies have shown that proteins cannot perform these functions alone in cells, and need to be combined with ligands to perform functions. background: As the basic material of life, protein regulates physiological activities such as material in and out of cells, signal transduction, metabolism and so on. However, studies have shown that proteins cannot perform these functions alone in cells, and need to be combined with ligands to perform functions. Objective: The discovery of drugs is based on this mechanism to be developed. But at present, it is difficult to discover new drugs through biological experiments, which lead to high cost, deteriorate the environment and make the human body more resistant to drugs. objective: The discovery of drugs is based on this mechanism to be developed. But at present, it is difficult to discover new drugs through biological experiments, which lead to cost high, deteriorate the environment and make human body more resistant to drugs. The rapid development of computer can assist researchers to screen out potential drugs that can bind to proteins in advance. In the past few decades, most of drug-target affinity prediction methods have disadvantages in high requirements on data set and difficulties to predict the binding strength of drug-target. Method: The rapid development of computers can assist researchers in screening potential drugs that can bind to proteins in advance. In the past few decades, most of the protein drug-target affinity prediction methods have had disadvantages of high requirements on data set and difficulties to predict the binding strength of drug-target. method: This paper proposes a multi-level feature extraction model based on graph convolution to predict drug-target affinity. The model uses an integrated neural network of CNN and GNN to learn the characteristics of the input data of drug-targets. Results: This paper proposes a multilevel feature extraction model based on graph convolution to predict protein drug-target affinity. The model uses an integrated neural network of Text CNN and GNN to learn the characteristics of the input data of drug-targets. result: Experimental results on the benchmark datasets of Davis and Kiba showed that the proposed graph-based convolution network achieves good performance on drug-target affinity prediction. Conclusion: Experimental results on the benchmark datasets of Davis and Kiba showed that the proposed graph-based convolution network achieves good performance on drug-target affinity prediction. conclusion: The results showed that the multi-level feature extraction network model based on graph convolution (GCN-GAT-GCN) is more effective in learning molecular feature information. other: None

For the purpose of developing new drugs and repositioning existing ones, accurate drug-target affinity (DTA) prediction is essential. While graph neural networks are frequently utilized for DTA prediction, it is difficult for existing single-scale graph neural networks to access the global structure of compounds. We propose a novel DTA prediction model in this study, MAPGraphDTA, which uses an approach based on a multi-head linear attention mechanism that aggregates global features based on the attention weights and a multi-scale gated power graph that captures multi-hop connectivity relationships of graph nodes. In order to accurately extract drug target features, we provide a gated skip-connection approach in multiscale graph neural networks, which is used to fuse multiscale features to produce a rich representation of feature information. We experimented on the Davis, Kiba, Metz, and DTC datasets, and we evaluated the proposed method against other relevant models. Based on all evaluation metrics, MAPGraphDTA outperforms the other models, according to the results of the experiment. We also performed cold-start experiments on the Davis dataset, which showed that our model has good prediction ability for unseen drugs, unseen proteins, and cases where neither drugs nor proteins has been seen.

Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substantially reduce development time and costs. However, most of them rely on prior knowledge, either by drawing on the structure and properties of known molecules to generate similar candidate molecules or extracting information on the binding sites of protein pockets to obtain molecules that can bind to them. In this paper, DeepTarget, an end-to-end DL model, was proposed to generate novel molecules solely relying on the amino acid sequence of the target protein to reduce the heavy reliance on prior knowledge. DeepTarget includes three modules: Amino Acid Sequence Embedding (AASE), Structural Feature Inference (SFI), and Molecule Generation (MG). AASE generates embeddings from the amino acid sequence of the target protein. SFI inferences the potential structural features of the synthesized molecule, and MG seeks to construct the eventual molecule. The validity of the generated molecules was demonstrated by a benchmark platform of molecular generation models. The interaction between the generated molecules and the target proteins was also verified on the basis of two metrics, drug–target affinity and molecular docking. The results of the experiments indicated the efficacy of the model for direct molecule generation solely conditioned on amino acid sequence.

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

DrugForm-DTA: Towards real-world drug-target binding affinity model

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug-target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug discovery research since researchers started using deep learning as a potent tool for DTB prediction. In particular, we examine how methodologies have evolved, starting with early heterogeneous network-based approaches, progressing to graph-based approaches that were widely accepted, followed by modern attention-based architectures, and finally, the most recent multimodal approaches. We also provide case studies utilizing an extensive compound library against specific protein targets implicated in critical cancer pathways to demonstrate the usefulness of these approaches. In addition to summarizing the latest developments in DTB prediction models, this review also identifies their drawbacks. It also highlights the outlook for the DTB prediction domain and future research directions. Combined, these studies present a more comprehensive view of how deep learning offers a quantitative framework for researching drug-target relationships, speeding up the identification of new drug candidates and making it easier to identify possible DTBs.