Traditional drug discovery is a time-consuming and costly endeavor. To address this challenge, this research developed a deep-learning-based conditional generative model. This model is designed to generate small molecules guided by specific protein sequences (e.g., EGFR), endowing them with targeted pharmacological activity, high novelty, uniqueness, and promising drug-likeness, thereby accelerating pan-cancer drug molecule design. The study employed a hybrid architecture combining Graph Neural Networks (GNNs) for processing molecular structures and a Transformer model for encoding protein sequences, which together provide conditional guidance for the generation process. The model's performance was evaluated on a validation set using metrics such as training loss curves, Mean Squared Error (MSE), and$\mathrm{R}^{2}$(coefficient of determination). The quality of the generated molecules was assessed based on their validity, uniqueness, novelty, and the number of violations against Lipinski's Rule of Five. The results indicate that the model training was stable and convergent, demonstrating good generalization ability. The generated molecules exhibited excellent novelty (100%) and drug-likeness (with an average of zero Lipinski's rule violations), while achieving 100% uniqueness. Although the validity rate was 22%, suggesting room for improvement, these findings collectively underscore the model's significant potential for conditional innovative drug molecule design. This work lays a solid foundation for future model optimization to enhance generation efficiency and explore its broader applications in drug discovery.

Grasping the intrinsic properties of protein structure is crucial for comprehending relevant biological mechanisms, with protein flexibility standing out as a critical aspect. Therefore, the prediction of protein flexibility is of great importance in understanding molecular mechanisms. We propose a deep learning method named ResaPred, which extracts diverse features from protein sequences, such as secondary structure, torsion angle, solvent accessibility, etc. ResaPred is a novel deep network based on a modified 1D residual module and a self-attention mechanism, which effectively extracts deep key features related to flexibility. The modified 1D residual module consists of three convolution layers, with batchnorm and relu layers added after each layer to prevent gradient explosion or vanishing. Incorporating self-attention mechanisms into neural network architectures introduces a significant advantage in capturing long-range dependencies within sequential data. We conduct experiments on the non-strict and strict cases, and achieve state-of-the-art results in predicting flexibility compared to existing methods. Furthermore, we extended our analysis to explore the correlation between protein secondary structure and solvent accessibility with flexibility. Finally, we used two important viral proteins as case studies, confirming the effectiveness of our method in recognizing the flexibility of protein structures.

Biological markers (i.e., biomarkers) are the key to predicting disease states and revealing the molecular mechanisms in precision medicine of complex diseases (e.g., cancer). With the advancement of high-throughput sequencing technology, there has been a significant increase in the volume and diversity of known disease omics data, where many methods have been developed to identify potential disease biomarkers (DBs) for mining the complex dynamics. As emerging artificial intelligence techniques, evolutionary computation (EC) has found extensive application in the identification of DBs, making significant achievements in mining disease omics data. However, there is currently no survey or analysis available of the existing EC methods to identify DBs on the disease omics data, resulting in missed opportunities to enhance performance and achieve successful applications in precision medicine. This article aims to present a comprehensive overview of the latest EC methods for mining the dynamics of DBs, including the summary of biomolecular omics datasets, the classification of the EC methods for DB discovery, and performance comparisons of the typical EC methods. Additionally, this article discusses challenges and potential future directions of the EC methods in the identification of DBs, providing directions and prospects for future research.

At present, existing multi-receiver authentication (MRA) schemes can only handle situations where the capacities of all receivers in the schemes are the same. However, in reality, different receivers may need to have different storage capacities. In this paper, inspired by the secret sharing scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings where each participant holds the share with different sizes, we propose three new constructions of unconditionally secure MRA schemes for multiple messages using the CRT for polynomial rings, including a$(k,n)$-threshold MRA scheme, a$(k,n,\omega)$-weighted threshold MRA scheme, and a$(\mathcal {Q},\mathcal {F})$-general MRA scheme. As far as we know, our proposed MRA schemes are the first MRA schemes with different storage capacities for different receivers and the first ones based on the CRT for polynomial rings. Moreover, the proposed schemes can be seen as extensions of the MRA scheme in Safavi-Naini and Wang. In particular, as for our$(\mathcal {Q},\mathcal {F})$-general MRA scheme, it has generally more communication complexity and much less computation complexity than the existing$(\mathcal {Q},\mathcal {F})$-general MRA scheme.

Combination therapy of drugs showed significant potential in treating complex diseases by overcoming drug resistance and improving therapeutic efficacy. However, due to the rapid increase in the number of available drugs, the cost and time required for experimentally screening synergistic drug combinations became increasingly burdensome. In this work, we proposed a novel drug synergy prediction model called GraphTranSynergy, which utilized graph transformer and BiLSTM to capture the molecular structure of drugs and gene expression features of cell lines. GraphTranSynergy extracted graphical features of drug pairs through the graph transformer module and integrated information from the BiLSTM module to extract useful features from gene expression profiles of cell lines. The final prediction of drug synergy was made through a fully connected neural network. Our model achieved AUC and PRAUC scores of 0.94, outperforming most existing models. Independent test results demonstrated that GraphTranSynergy exhibited superior generalization ability on the AstraZeneca dataset, particularly excelling in ACC and TPR metrics. Through a series of experiments and analyses, our model not only improved prediction accuracy but also demonstrated advantages in biological interpretability.

At present, existing multi-receiver authentication (MRA) schemes can only handle situations where the capabilities of all receivers are the same. However, in reality, different receivers may need to have different storage capabilities. In this paper, inspired by the secret sharing scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings where distinct participants save shares with distinct sizes, we propose a new unconditionally secure MRA scheme for multiple messages by the same technique. As far as we know, our MRA scheme is the first MRA scheme with different storage capacities for different receivers and the first one based on the CRT for polynomial rings supporting general access structures. In contrast to the existing general MRA scheme, although our MRA scheme has more communication complexity, it has less computation complexity.

In this paper, for the first time, we propose a new explicit hierarchical threshold secret sharing (HTSS) scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings, where the participant set is divided into disjoint subsets and the threshold of a superior subset is less than the threshold of an inferior subset. In addition, we present a rigorous security analysis to show that our HTSS scheme is both perfect and ideal. Moreover, a toy example of our HTSS scheme is given to enable readers to better understand our construction. By comparison, it appears that our scheme is the first CRT-based HTSS for polynomial rings and also the first ideal and perfect CRT-based HTSS scheme, which is easier to construct than its counterpart for integer rings, where different participants hold shares of different sizes. Besides, our HTSS can also distribute shares of the same size, similar to other HTSS.

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

Transcription factors are a class of protein factors that bind directly or indirectly to RNA polymerases and regulate the initiation of transcription by recognizing cis-acting elements in the DNA sequence. The prediction of transcription factor binding sites is an important part of the study of gene transcriptional regulation. Therefore, accurate prediction of TFBS helps one to understand and study the spatiotemporal nature of transcriptional regulation of target genes by different transcription factors. In recent years, an increasing number of deep learning methods have been used to predict transcription factor binding sites, however, existing methods still much room to improve performance. In this paper, we present a deep learning framework combining convolutional neural networks and recurrent neural networks to predict transcription factor binding sites, called DeepGenBind, for the systematic identification of transcription factor binding sites from DNA sequences. The novelty of our proposed approach relies on two key aspects: (1) the framework combines a three-layer parallel convolutional neural network CNN with a two-layer LSTM to efficiently extract useful features from large-scale genomic sequences obtained by high-throughput sequencing techniques (2) the use of k-mer coding to transform DNA sequences, with the transformed short sequences allowing for better data reading. Experimental results on 165 datasets from ENCODE show that DeepGenBind outperforms several other state-of-the-art methods in identifying transcription factor binding sites. In addition, we tested the effect of varying the k-mer vector length on model performance, demonstrating the variation in model performance under different k-mer related parameter settings. Overall, DeepGenBind is a useful tool for the cost-effective and accurate identification of potential transcription factor binding sites in biological genomes.

In the drug discovery process and repurposing of existing drugs, accurately identifying ligands with high binding affinity to proteins is a very critical step. However, it sinks a lot of time and resources to detect the protein-ligand binding affinity through biological experiments. Therefore, it is very necessary to develop an accurate and reliable computational method to predict the binding affinity between protein and ligand. At present, some computational methods have been proposed to predict the protein-ligand binding affinity, but the absence of protein-ligand complexes structures restricts some predictive methods that require input the complexes structures. In this paper, a novel deep-learning-based method is proposed, named DPLA, to predict binding affinity by integrating multilevel information of protein and ligand. More specifically, our model extracted some important information, such as sequence representation, structural property representation of amino acids in protein and protein binding pocket, MACCS key ligand molecular fingerprint and ligand molecular network features. This method was tested on the PDBbind core set, and we compared it with some recent state-of-art protein-ligand affinity prediction methods. The excellent performance shows that DPLA is an accurate and reliable method for affinity prediction.

Abstract Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.

Multi‐receiver authentication plays an important role in network security. Many researchers have studied the constructions and the properties of the multi‐receiver authentication scheme. However, most of these schemes treat the capability of all the receivers equally. In practice, receivers may have different other than equal powers in many cases. The authors consider the new scenario in the multi‐receiver authentication. The authors propose a multi‐receiver authentication scheme with hierarchical structure among the receivers. The authors construct an unconditionally secure multi‐receiver authentication code by using the Birkhoff interpolation. The authentication scheme is also able to send multiple messages.

Advanced e-applications require comprehensive knowledge about their users’ preferences in order to provide accurate personalized services. In this paper, we propose to learn users’ preferences to product brands from their implicit feedbacks such as their searching and browsing behaviors in user Web browsing log data. The user brand preference learning problem is challenge since (1) the users’ implicit feedbacks are extremely sparse in various product domains; and (2) we can only observe positive feedbacks from users’ behaviors. In this paper, we propose a latent factor model to collaboratively mine users’ brand preferences across multiple domains simultaneously. By collective learning, the learning processes in all the domains are mutually enhanced and hence the problem of data scarcity in each single domain can be effectively addressed. On the other hand, we learn our model with an adaption of the Bayesian personalized ranking (BPR) optimization criterion which is a general learning framework for collaborative filtering from implicit feedbacks. Experiments with both synthetic and real world datasets show that our proposed model significantly outperforms the baselines.

In this demo, we present the Microsoft Complex Event Processing (CEP) Server, Microsoft CEP for short. Microsoft CEP is an event stream processing system featured by its declarative query language and its multiple consistency levels of stream query processing. Query composability, query fusing, and operator sharing are key features in the Microsoft CEP query processor. Moreover, the debugging and supportability tools of Microsoft CEP provide visibility of system internals to users. Web click analysis has been crucial to behavior-based online marketing. Streams of web click events provide a typical workload for a CEP server. Meanwhile, a CEP server with its processing capabilities plays a key role in web click analysis. This demo highlights the features of Microsoft CEP under a workload of web click events.