Deep-Learning-Based Drug–Target Interaction Prediction

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

3MTox: A motif-level graph-based multi-view chemical language model for toxicity identification with deep interpretation

Toxicity identification plays a key role in maintaining human health, as it can alert humans to the potential hazards caused by long-term exposure to a wide variety of chemical compounds. Experimental methods for determining toxicity are time-consuming, and costly, while computational methods offer an alternative for the early identification of toxicity. For example, some classical ML and DL methods, which demonstrate excellent performance in toxicity prediction. However, these methods also have some defects, such as over-reliance on artificial features and easy overfitting, etc. Proposing novel models with superior prediction performance is still an urgent task. In this study, we propose a motifs-level graph-based multi-view pretraining language model, called 3MTox, for toxicity identification. The 3MTox model uses Bidirectional Encoder Representations from Transformers (BERT) as the backbone framework, and a motif graph as input. The results of extensive experiments showed that our 3MTox model achieved state-of-the-art performance on toxicity benchmark datasets and outperformed the baseline models considered. In addition, the interpretability of the model ensures that the it can quickly and accurately identify toxicity sites in a given molecule, thereby contributing to the determination of the status of toxicity and associated analyses. We think that the 3MTox model is among the most promising tools that are currently available for toxicity identification.

Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods

We present an electrochemical platform designed to reduce time of Escherichia coli bacteria detection from 24 to 48-h to 30 min. The presented approach is based on a system which includes gallium-indium (eGaIn) alloy to provide conductivity and a hydrogel system to preserve bacteria and their metabolic species during the analysis. The work is dedicated to accurate and fast detection of Escherichia coli bacteria in different environments with the supply of machine learning methods. Electrochemical data obtained during the analysis is processed via multilayer perceptron model to identify i.e. predict bacterial concentration in the samples. The performed approach provides the effectiveness of bacteria identification in the range of 102-109 colony forming units per ml with the average accuracy of 97%. The proposed bioelectrochemical system combined with machine learning model is prospective for food analysis, agriculture, biomedicine.

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

A review of biophysical strategies to investigate protein-ligand binding: What have we employed?

The protein-ligand binding frequently occurs in living organisms and plays a crucial role in the execution of the functions of proteins and drugs. It is also an indispensable part of drug discovery and screening. While the methods for investigating protein-ligand binding are diverse, each has its own objectives, strengths, and limitations, which all influence the choice of method. Many studies concentrate on one or a few specific methods, suggesting that comprehensive summaries are lacking. Therefore in this review, these methods are comprehensively summarized and are discussed in detail: prediction and simulation methods, thermal and thermodynamic methods, spectroscopic methods, methods of determining three-dimensional structures of the complex, mass spectrometry-based methods and others. It is also important to integrate these methods based on the specific objectives of the research. With the aim of advancing pharmaceutical research, this review seeks to deepen the understanding of the protein-ligand binding process.

Drug-Target Interaction Prediction Based on an Interactive Inference Network

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.

PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry

Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation learning and property prediction, their performance could be further enhanced by incorporating three-dimensional (3D) structural geometry into two-dimensional (2D) molecular graph representation. In this study, we introduce the PointGAT model for quantum molecular property prediction, which integrates 3D molecular coordinates with graph-attention modeling. Comparison with other current models in molecular prediction tasks showed that PointGAT could provide higher predictive accuracy in various benchmark data sets from MoleculeNet, including ESOL, FreeSolv, Lipop, HIV, and 6 out of 12 tasks of the QM9 data set. To further examine PointGAT prediction of quantum mechanical (QM) energies, we constructed a C10 data set comprising 11,841 charged and chiral carbocation intermediates with QM energies calculated at the DM21/6-31G*//B3LYP/6-31G* levels. Notably, PointGAT achieved an R2 value of 0.950 and an MAE of 1.616 kcal/mol, outperforming even the best-performing graph neural network model with a reduction of 0.216 kcal/mol in MAE and an improvement of 0.050 in R2. Additional ablation studies indicated that incorporating molecular geometry into the model resulted in markedly higher predictive accuracy, reducing the MAE value from 1.802 to 1.616 kcal/mol. Moreover, visualization of PointGAT atomic attention weights suggested its predictions were interpretable. Findings in this study support the application of PointGAT as a powerful and versatile tool for quantum chemical property prediction that can facilitate high-accuracy modeling for fundamental exploration of chemical space as well as drug design and molecular engineering.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis

Introduction

In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research.

Methods

Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies.

Results and Discussion

In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions

A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by machine learning and deep neural networks, that allow the development of drug-target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug-target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

Application of artificial intelligence and machine learning in drug repurposing

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

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.

iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models

MOTIVATION

Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization.

RESULTS

We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the target graphs are created based on 3D structures using Alphafold2. This architecture is highly expressive in capturing substructures of the graph data. We use a cross-attention module to capture interaction information between the substructures of drugs and targets. To improve feature representations, we integrate features learned by models that are pre-trained on large unlabeled small molecule and protein datasets, respectively. We evaluate our model on three benchmark datasets, and it shows a consistent improvement on all baseline models in all datasets. We also run an experiment with previously unseen drugs or targets in the test set, and our model outperforms all of the baselines. Furthermore, the iNGNN-DTI can provide more insights into the interaction by visualizing the weights learned by the cross-attention module.

AVAILABILITY AND IMPLEMENTATION

The source code of the algorithm is available at https://github.com/syan1992/iNGNN-DTI.

Multi-party collaborative drug discovery via federated learning

In the field of drug discovery and pharmacology research, precise and rapid prediction of drug-target binding affinity (DTA) and drug-drug interaction (DDI) are essential for drug efficacy and safety. However, pharmacological data are often distributed across different institutions. Moreover, due to concerns regarding data privacy and intellectual property, the sharing of pharmacological data is often restricted. It is difficult for institutions to achieve the desired performance by solely utilizing their data. This urgent challenge calls for a solution that not only enhances collaboration between multiple institutions to improve prediction accuracy but also safeguards data privacy. In this study, we propose a novel federated learning (FL) framework to advance the prediction of DTA and DDI, namely FL-DTA and FL-DDI. The proposed framework enables multiple institutions to collaboratively train a predictive model without the need to share their local data. Moreover, to ensure data privacy, we employ secure multi-party computation (MPC) during the federated learning model aggregation phase. We evaluated the proposed method on two DTA and one DDI benchmark datasets and compared them with centralized learning and local learning. The experimental results indicate that the proposed method performs closely to centralized learning, and significantly outperforms local learning. Moreover, the proposed framework ensures data security while promoting collaboration among institutions, thereby accelerating the drug discovery process.

Potential Rheumatoid Arthritis-Associated Interstitial Lung Disease Treatment and Computational Approach for Future Drug Development

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and showed efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with anti-fibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild anti-fibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.

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.

CCL-DTI: contributing the contrastive loss in drug-target interaction prediction

BACKGROUND

The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.

RESULTS

In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets.

CONCLUSIONS

Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.

KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.

Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve.

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.

Multimodal multi-task deep neural network framework for kinase-target prediction

Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods for kinase selectivity identification use biochemical assays, which are very useful but limited by the protein available. The lack of kinase selectivity can exert benefits but also can cause adverse effects. With the explosion of the dataset for kinase activities, current computational methods can achieve accuracy for large-scale selectivity predictions. Here, we present a multimodal multi-task deep neural network model for kinase selectivity prediction by calculating the fingerprint and physiochemical descriptors. With the multimodal inputs of structure and physiochemical properties information, the multi-task framework could accurately predict the kinome map for selectivity analysis. The proposed model displays better performance for kinase-target prediction based on system evaluations.

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.

LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening

Background

Drug-target interaction (DTI) is a vital drug design strategy that plays a significant role in many processes of complex diseases and cellular events. In the face of challenges such as extensive protein data and experimental costs, it is suggested to apply bioinformatics approaches to exploit potential interactions to design new targeted medications. Different data and interaction types bring difficulties to study involving incompatible and heterology formats. The analysis of drug-target interactions in a comprehensive and unified model is a significant challenge.

Method

Here, we propose a general method for predicting interactions between small-molecule drugs and protein targets, Large-scale Drug target Screening Convolutional Neural Network (LDS-CNN), which used unified encoding to achieve the calculation of the different data formats in an integrated model to realize feature abstraction and potential object prediction.

Result

On 898,412 interaction data involving 1683 small-molecule compounds and 14,350 human proteins from 8.8 billion records, the proposed method achieved an area under the curve (AUC) of 0.96, an area under the precision-recall curve (AUPRC) of 0.95, and an accuracy of 90.13%. The experimental results illustrated that the proposed method attained high accuracy on the test set, indicating its high predictive ability in drug-target interaction prediction. LDS-CNN is effective for the prediction of large-scale datasets and datasets composed of data with different formats.

Conclusion

In this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network

Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.

GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47

Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 μM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.

What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques

Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.

Artificial intelligence & clinical nutrition: What the future might have in store

Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.

AmorProt: Amino Acid Molecular Fingerprints Repurposing-Based Protein Fingerprint

As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data-driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing-based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree-based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature-based methods, (4) feature importance analysis, and (5) protein space analysis. Consequently, the significantly improved model performance and data-set-independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands.

A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction

Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.

Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Drug-Target Interaction Prediction via Graph Auto-Encoder and Multi-Subspace Deep Neural Networks

Computational prediction of drug-target interaction (DTI) is important for the new drug discovery. Currently, the deep neural network (DNN) has been widely used in DTI prediction. However, parameters of the DNN could be insufficiently trained and features of the data could be insufficiently utilized, because the DTI data is limited and its dimension is very high. To deal with the above problems, in this paper, a graph auto-encoder and multi-subspace deep neural network (GAEMSDNN) is designed. GAEMSDNN enhances its learning ability with a graph auto-encoder, a subspace layer and an ensemble layer. The graph auto-encoder can preserve the reconstruction information. The subspace layer can obtain different strong feature subsets. The ensemble layer in the GAEMSDNN can comprehensively utilize these strong feature subsets in a unified optimization framework. As a result, more features can be extracted from the network input and the DNN network can be better trained. In experiments, the results of GAEMSDNN are significantly improved compared to the previous methods, which validates the effectiveness of our strategies.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Applications of machine learning in microbial natural product drug discovery

INTRODUCTION

Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development.

AREAS COVERED

This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery.

EXPERT OPINION

Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.

Comparing methods for drug-gene interaction prediction on the biomedical literature knowledge graph: performance versus explainability

This paper applies different link prediction methods on a knowledge graph generated from biomedical literature, with the aim to compare their ability to identify unknown drug-gene interactions and explain their predictions. Identifying novel drug-target interactions is a crucial step in drug discovery and repurposing. One approach to this problem is to predict missing links between drug and gene nodes, in a graph that contains relevant biomedical knowledge. Such a knowledge graph can be extracted from biomedical literature, using text mining tools. In this work, we compare state-of-the-art graph embedding approaches and contextual path analysis on the interaction prediction task. The comparison reveals a trade-off between predictive accuracy and explainability of predictions. Focusing on explainability, we train a decision tree on model predictions and show how it can aid the understanding of the prediction process. We further test the methods on a drug repurposing task and validate the predicted interactions against external databases, with very encouraging results.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M^pro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the M^pro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug-Target Binding Affinity Prediction

The accurate prediction of drug-target binding affinity (DTA) is an essential step in drug discovery and drug repositioning. Although deep learning methods have been widely adopted for DTA prediction, the complexity of extracting drug and target protein features hampers the accuracy of these predictions. In this study, we propose a novel model for DTA prediction named MSGNN-DTA, which leverages a fused multi-scale topological feature approach based on graph neural networks (GNNs). To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. Our approach constructs drug atom graphs, motif graphs, and weighted protein graphs to fully extract topological information and provide a comprehensive understanding of underlying molecular interactions from multiple perspectives. Experimental results on two benchmark datasets demonstrate that MSGNN-DTA outperforms the state-of-the-art models in all evaluation metrics, showcasing the effectiveness of the proposed approach. Moreover, the study conducts a case study based on already FDA-approved drugs in the DrugBank dataset to highlight the potential of the MSGNN-DTA framework in identifying drug candidates for specific targets, which could accelerate the process of virtual screening and drug repositioning.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.