Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins

PocketDTA: A pocket-based multimodal deep learning model for drug-target affinity prediction

Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function". PocketDTA introduces the pocket graph structure that encodes protein residue features pretrained using a biological language model as nodes, while edges represent different protein sequences and spatial distances. This approach overcomes the limitations of lack of spatial information in traditional prediction models with only protein sequence input. Furthermore, PocketDTA employs relational graph convolutional networks at both atomic and residue levels to extract structural features from drugs and proteins. By integrating multimodal information through deep neural networks, PocketDTA combines sequence and structural data to improve affinity prediction accuracy. Experimental results demonstrate that PocketDTA outperforms state-of-the-art prediction models across multiple benchmark datasets by showing strong generalization under more realistic data splits and confirming the effectiveness of pocket-based methods for affinity prediction.

Deep learning methods for proteome-scale interaction prediction

Proteome-scale interaction prediction is essential for understanding protein functions and disease mechanisms. Traditional experimental methods are often limited by scale and complexity, driving the need for computational approaches. Deep learning has emerged as a powerful tool, enabling high-throughput, accurate predictions of protein interactions. This review highlights recent advances in deep learning methods for protein-protein and protein-ligand interaction screening, along with datasets used for model training. Despite the progress with deep learning, challenges such as data quality and validation biases remain. We also discuss the increasing importance of integrating structural information to enhance prediction accuracy and how structure-based deep learning approaches can help overcome current limitations, ultimately advancing biological research and drug discovery.

SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

Predicting drug protein interactions based on improved support vector data description in unbalanced data

Introduction

Predicting drug-protein interactions is critical in drug discovery, but traditional laboratory methods are expensive and time-consuming. Computational approaches, especially those leveraging machine learning, are increasingly popular. This paper introduces VASVDD, a multi-step method to predict drug-protein interactions. First, it extracts features from amino acid sequences in proteins and drug structures. To address the challenge of unbalanced datasets, a Support Vector Data Description (SVDD) approach is employed, outperforming standard techniques like SMOTE and ENN in balancing data. Subsequently, dimensionality reduction using a Variational Autoencoder (VAE) reduces features from 1074 to 32, improving computational efficiency and predictive performance.

Methods

The proposed method was evaluated on four datasets related to enzymes, G-protein-coupled receptors, ion channels, and nuclear receptors. Without preprocessing, the Gradient Boosting Classifier showed bias towards the majority class. However, balancing and dimensionality reduction significantly improved accuracy, sensitivity, specificity, and F1 scores. VASVDD demonstrated superior performance compared to other dimensionality reduction methods, such as kernel principal component analysis (kernel PCA) and Principal Component Analysis (PCA), and was validated across multiple classifiers, achieving higher AUROC values than existing techniques.

Results

The results highlight VASVDD's effectiveness and generalizability in predicting drug-target interactions. The method outperforms state-of-the-art techniques in terms of accuracy, robustness, and efficiency, making it a promising tool in bioinformatics for drug discovery.

Conclusion

The datasets analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request and source code are available on GitHub: https://github.com/alirezakhorramfard/vasvdd.

GraphPBSP: Protein binding site prediction based on Graph Attention Network and pre-trained model ProstT5

Protein-protein/peptide interactions play crucial roles in various biological processes. Exploring their interactions attracts wide attention. However, accurately predicting their binding sites remains a challenging task. Here, we develop an effective model GraphPBSP based on Graph Attention Network with Convolutional Neural Network and Multilayer Perceptron for protein-protein/peptide binding site prediction, which utilizes various feature types derived from protein sequence and structure including interface residue pairwise propensity developed by us and sequence embeddings obtained from a new pre-trained model ProstT5, alongside physicochemical properties and structural features. To our best knowledge, ProstT5 sequence embeddings and residue pairwise propensity are first introduced for protein-protein/peptide binding site prediction. Additionally, we propose a spatial neighbor-based feature statistic method for effectively considering key spatially neighboring information that significantly improves the model's prediction ability. For model training, a multi-scale objective function is constructed, which enhances the learning capability across samples of the same or different classes. On multiple protein-protein/peptide binding site test sets, GraphPBSP outperforms the currently available state-of-the-art methods with an excellent performance. Additionally, its performances on protein-DNA/RNA binding site test sets also demonstrate its good generalization ability. In conclusion, GraphPBSP is a promising method, which can offer valuable information for protein engineering and drug design.

Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention

Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate screening by swiftly identifying potential active molecules. The popular deep learning methods in bioactivity prediction primarily model the ligand structure-bioactivity relationship under the premise of Quantitative Structure-Activity Relationship (QSAR). However, bioactivity is determined by multiple factors, including not only the ligand structure but also drug-target interactions, signaling pathways, reaction environments, pharmacokinetic properties, and species differences. Our study first integrates drug-target interactions into bioactivity prediction using protein-ligand complex data from molecular docking. We devise a Drug-Target Interaction Graph Neural Network (DTIGN), infusing interatomic forces into intermolecular graphs. DTIGN employs multi-head self-attention to identify native-like binding pockets and poses within molecular docking results. To validate the fidelity of the self-attention mechanism, we gather ground truth data from crystal structure databases. Subsequently, we employ these limited native structures to refine bioactivity prediction via semi-supervised learning. For this study, we establish a unique benchmark dataset for evaluating bioactivity prediction models in the context of protein-ligand complexes, showcasing the superior performance of our method (with an average improvement of 27.03%) through comparison with 9 leading deep learning-based bioactivity prediction methods.

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction

Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions. The EMPDTA framework consists of three main modules: pocket online detection, multimodal representation learning for affinity prediction, and multi-task joint training. The performance and potential of the proposed framework have been validated across diverse benchmark datasets, achieving robust results in both tasks. Furthermore, the visualization results of the predicted pockets demonstrate accurate pocket detection, confirming the effectiveness of our framework.

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.