The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.