Implementation of Breadth-First Search Parallel to Predict Drug-Target Interaction in Plant-Disease Graph

Introductory Chapter: The Emerging Corner of the Omics Studies for Rational Drug Design

“…Parallelization in the Drug Biomanufacturing with incremental optimization will facilitate the pipeline of therapeutic agent development [71,72]. Such biomanufacturing efforts will be enhanced with the implementation of Artificial Intelligence (AI) for massive drug screening that could be beneficial for multicomponents drug lead such as herbal medication, and machine-learning based implementation for such pipelines has been devised accordingly in COVID-19 leads development [73][74][75][76]. The extensive utilization of the common data science methods in bioinformatics, such as machine learning, will eventually provide insight that the management of life sciences data requires more than just becoming application users [77].…”

Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach

“…Parallel drug-target interaction (DTI) research has been carried out using several schemes, including breadth-first search (BFS) [6], molecular docking with GPU [7], and BINDSURF, which is a virtual screening methodology to find a protein binding site for a ligand [8]. In [6], BFS is used to predict drug-target interaction in a graph and is optimized by parallelization using CUDA, which gained a speed-up of 51.33 times by using four threads. In [7], a novel molecular docking approach is proposed and optimized by using a heterogeneous implementation based on multicore CPUs and multiple GPUs.…”