MABBI – Research conducted by Alvin Reinaldo, Wisnu Ananta Kusuma, Hendra Rahmawan and Yeni Herdiyeni from IPB University entitled Implementation of breadth-first search parallel to predict drug-target interaction in plant-disease graph
Degenerative diseases are often treated by using chemical drugs. But the use of chemical drugs has the risk to cause side effects. Herbal medicine has smaller side effects compared to the chemical drugs. The development of drugs for degenerative diseases requires large costs with several side effects and risks. Drug repurposing can minimize costs, risks, and time needed to develop drug. The target-based objectives of computational method was used to search for compound-protein similarities and predict drug-protein interactions. This research uses breadth-first search algorithm that was implemented in parallel computation. The algorithm was compared in performance by using sequential and parallel computing. The results show that the parallelization based on BFS using 4 threads size require 46.299 seconds to complete searching process and obtain speed up of 51.33 times compared to the sequential algorithm (Tri/MABBI).
Read more: https://ieeexplore.ieee.org/document/9243216/
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