Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

The interaction between three flavonoids, i.e., Luteolin (LTL), Quercetin (QCT), and Naringenin (NGN) and bovine lactoferrin (BLF) at pH 7.4 was investigated by fluorescence quenching spectra, synchronous fluorescence spectra, and UV-visible absorption spectra. The results indicate the fluorescence of BLF quenched by Luteolin (LTL), Quercetin (QCT), and Naringenin (NGN) via static quenching. The main force between QCT and LTL with BLF was van der Waals interactions and hydrogen bonds. Electrostatic interactions played a major role in the binding process of interaction between NGN and BLF. Synchronous fluorescence was used to study the conformational changes of BLF. The values of binding constant (Ka) and number of binding sites (n) at different temperatures (300K, 305K, 310K) were also calculated, respectively. The results of corresponding thermodynamic parameters as well as binding distance between BLF and LTL, QCT, or GNG were obtained. These results implied that Luteolin (LTL), Quercetin (QCT), and Naringenin (NGN) could provide important guides for compound quantity (e.g., medicine dosage) and the design of new compounds (or drugs).

The flavonoid dihydromyricetin (DMY) is the main component of Ampelopsis grossedentata (Hand‐Mazz) W. T. Wang (AG), a daily beverage and folk medicine used in Southern China to treat jaundice hepatitis, cold fever, and sore throat. Recently, DMY and AG were shown to have a beneficial effect on lipid metabolism disorder. However, the mechanisms of how DMY and AG protect the liver during lipid metabolism disorder remain unclear. In this study, we first analyzed the chemical compounds of AG by HPLC‐DAD‐ESI‐IT‐TOF‐MSⁿ. Of the 31 compounds detected, 29 were identified based on previous results. Then, the effects of DMY and AG on high‐fat diet hamster livers were studied and the metabolite levels and metabolic pathway activity of the liver were explored by ¹H NMR metabolomics. Compared to the high‐fat diet group, supplementation of AG and DMY attenuated the high‐fat‐induced increase in body weight, liver lipid deposition, serum triglycerides and total cholesterol levels, and normalized endogenous metabolite concentrations. PCA and PLS‐DA score plots demonstrated that while the metabolic profiles of hamsters fed a high‐fat diet supplemented with DMY or AG were both far from those of hamsters fed a normal diet or a high‐fat diet alone, they were similar to each other. Our data suggest that the underlying mechanism of the protective effect of DMY and AG might be related to an attenuation of the deleterious effect of high‐fat diet‐induced hyperlipidemia on multiple metabolic pathways including amino acid metabolism, ketone body metabolism, energy metabolism, tricarboxylic acid cycle, and enhanced fatty acid oxidation.

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.