Title:Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery
Volume: 22
Issue: 6
关键词:
机器学习、人工智能、大数据、虚拟筛选、精准医学、药物发现
摘要: Artificial Intelligence revolutionizes the drug development process that can quickly identify
potential biologically active compounds from millions of candidate within a short period. The present
review is an overview based on some applications of Machine Learning based tools, such as
GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine
(SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages
of drug designing and development. These techniques can be employed in SNP discoveries, drug
repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure-
based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship
(QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance
in indicating that the classification model will have great applications on human intestinal absorption
(HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and
RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line,
DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also
used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus
(DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful
and efficient way to deal with the huge amounts of generated data from modern drug discovery to
model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.
