Bayesian ranking-based drug-target relationship prediction has achieved good results, but it ignores the relationship between drugs of the same target. A new method is proposed for drug-target relationship prediction based on groups by Appling Bayesian. According to the reality that drugs interacting with a specific target have similarities, a grouping strategy was introduced to make these similar drugs interact. A theoretical model based on the grouping strategy is derived in this study. The method is compared with five typical methods on five publicly available datasets and produces superior results to the compared methods. The impact of grouping interaction on the Bayesian ranking approach is examined in this study to create a grouped medication set; comparable pharmaceuticals that interact with the same target are first grouped based on this reality. Then, based on the grouped drug set, new hypotheses were put forth and the conceptual approach of grouped Bayesian ranking was constructed. Finally, to predict novel medications and targets, the article also includes neighbor information. The associated studies demonstrate that the strategy presented in this study outperforms the conventional performance techniques. Plans for further performance improvement through the creation of new comparable grouping objectives are included in future work.

A new algorithm for practical Byzantine fault tolerance (PBFT), called score-PBFT or S-PBFT, is proposed to solve the problems of high communication overhead and low algorithm efficiency. This algorithm is based on the characteristics of the consortium chain. The scoring mechanism for nodes is added. All the nodes are broken up into consensus nodes, candidate nodes, and early nodes. To make sure the consensus nodes are as reliable as possible, the nodes are changed dynamically based on how each node is behaving. Improved: the election method for the controller node has been changed. The node’s score and behaviour are used as the election basis to make the algorithm more stable. In this paper, we want to improve the consensus protocol’s execution process, cut down on how many nodes are involved in the consensus process, simplify it, and make it more efficient. Results show that, when compared with the PBFT algorithm, the S-PBFT algorithm has a shorter consensus delay, less communication overhead and throughput, and better consensus node reliability.