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Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that's why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers. Actually, a dry lab environment focusing more on computational methods of interaction prediction can be helpful in limiting search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is proposed that called SRX-DTI. In the first stage, combination of various descriptors from protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature vectors. A major challenge in this application is the imbalanced data due to the lack of known interactions, in this regard, in the second stage, the One-SVM-US technique is proposed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection algorithm, coupled with a random forest (RF) classifier is developed to maximize the predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal features. Finally, balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves higher performance than other existing methods in predicting DTIs. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI. [ABSTRACT FROM AUTHOR]

Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher's score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction [ABSTRACT FROM AUTHOR]

Identification of drug-protein interactions plays an important role in drug discovery. Development of new calculation methods, which have high accuracy solve the problems related to the previous methods, which were expensive and time-consuming. In this article, a new model for drug-protein interactions, and a new mapping approach to represent drug-protein sequences are proposed. The proposed model consists of four parts: drug and protein descriptor section, Drug CNN and Protein CNN sections, Encoder section and classification section. In this method, first the data is prepared. At this stage, the totals are equal to each other. Then in the next step using the k-mers method and Chaos Game, the sequence of drug and protein becomes an image. In the next step, the image is used to train CNN models. These images serve as the input of independent networks for the drug and are considered as a protein. These networks are used to extract feature from drug and protein. In the last layer of these networks, features extracted from drug and protein sequences combine with each other. After concatenating, the number of features will raise. To reduce the number of features and to extract more efficient features, a Variational Autoencoder is used. In the last step, this combined feature vector is used to train machine learning models. The proposed method has been tested and evaluated on 6 standard data sets. The results of the experiments show that the proposed method has an acceptable performance compared to other methods in this data set. • An enhanced Encoder-decoder algorithm is used for dimension reduction. • A two-stage hybrid method is proposed that is based on feature extraction and feature reduction. • The proposed method is evaluated using several datasets. [ABSTRACT FROM AUTHOR]