The cytochrome P450 (CYP450) enzymes, heme-containing monooxygenases, are indispensable in metabolizing a broad range of drugs and other xenobiotics. Nonetheless, specific chemical entities (molecules) can inhibit these enzymes, resulting in undesirable drug-drug interactions, alterations in pharmacokinetics, and potentially compromising the efficacy and safety of drugs. Therefore, it is critical to identify molecules exhibiting inhibitory activities toward the five majors human CYP450 enzymes (1A2, 2C9, 2C19, 2D6, and 3A4) in the preliminary phases of drug development. This study presents a novel deep-learning model, MuMCyp_Net, for predicting CYP450 inhibition by small molecules. The architecture of MuMCyp_Net integrates a convolutional neural network (CNN) with an Attention mechanism, a bi-directional gated recurrent unit (biGRU) network, and a deep neural network (DNN) to efficiently process local chemical context information and global molecular properties of molecules. A total of 25,753 distinct compound molecules were utilized to train and evaluate the model’s performance. The proposed model demonstrates competitive performance as indicated by matthew’s correlation coefficients (MCC), ranging from 0.63 to 0.68, an accuracy rate between 0.82 and 0.90, and an AUC of 0.86 to 0.92. This novel approach shows considerable promise in accurately identifying CYP450 inhibitors, contributing to safer and more effective drug development. To further utilize the potential of the proposed model, a freely accessible web server tool for the virtual screening of molecules was developed, which will aid in identifying CYP450 inhibitors from non-inhibitors. The web-based tool can be accessed at https://mumcypnet.streamlit.app/.

Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.

Recognizing the offensive, abusive, and profanity of multimedia content on the web has been a challenge to keep the web environment for user’s freedom of speech. As profanity filtering function has been developed and applied in text, audio, and video context in platforms such as social media, entertainment, and education, the number of methods to trick the web-based application also has been increased and became a new issue to be solved. Compared to commonly developed toxic content detection systems that use lexicon and keyword-based detection, this work tries to embrace a different approach by the meaning of the sentence. Meaning representation is a way to grasp the meaning of linguistic input. This work proposed a data-driven approach utilizing Abstract meaning Representation to extract the meaning of the online text content into a convolutional neural network to detect level profanity. This work implements the proposed model in two kinds of datasets from the Offensive Language Identification Dataset and other datasets from the Offensive Hate dataset merged with the Twitter Sentiment Analysis dataset. The results indicate that the proposed model performs effectively, and can achieve a satisfactory accuracy in recognizing the level of online text content toxicity.

BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-related data to achieve successful solutions. Modeling the properties and functions of proteins is important but challenging, especially when dealing with predictions of the sequence type. RESULT: We propose a method to model compounds and proteins for compound-protein interaction prediction. A graph neural network is used to represent the compounds, and a convolutional layer extended with a bidirectional recurrent neural network framework, Long Short-Term Memory, and Gate Recurrent unit is used for protein sequence vectorization. The convolutional layer captures regulatory protein functions, while the recurrent layer captures long-term dependencies between protein functions, thus improving the accuracy of interaction prediction with compounds. A database of 7000 sets of annotated compound protein interaction, containing 1000 base length proteins is taken into consideration for the implementation. The results indicate that the proposed model performs effectively and can yield satisfactory accuracy regarding compound protein interaction prediction. CONCLUSION: The performance of GCRNN is based on the classification accordiong to a binary class of interactions between proteins and compounds The architectural design of GCRNN model comes with the integration of the Bi-Recurrent layer on top of CNN to learn dependencies of motifs on protein sequences and improve the accuracy of the predictions.