Machine Learning Methods to Predict the Terrestrial and Marine Origin of Natural Products

2021 ◽  
Author(s):  
Florbela Pereira
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Learning Methods ◽  
Marine Origin ◽  
Machine Learning Methods

scholarly journals Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2074
Author(s):  
Yuting Liu ◽  
Mengzhou Bi ◽  
Xuewen Zhang ◽  
Na Zhang ◽  
Guohui Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Inhibitory Activity ◽  
Predictive Accuracy ◽  
Information Gain ◽  
High Reliability ◽  
Learning Methods ◽  
Test Set ◽  
Machine Learning Methods ◽  
Ck2 Inhibitors

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors.

scholarly journals Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaocong Pang ◽  
Weiqi Fu ◽  
Jinhua Wang ◽  
De Kang ◽  
Lvjie Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Estrogen Receptor ◽  
Natural Products ◽  
Molecular Docking ◽  
Luciferase Reporter ◽  
Luciferase Reporter Gene ◽  
Learning Methods ◽  
Test Set ◽  
Machine Learning Methods ◽  
Gene Assay

Estrogen receptor α (ERα) is a successful target for ER-positive breast cancer and also reported to be relevant in many other diseases. Selective estrogen receptor modulators (SERMs) make a good therapeutic effect in clinic. Because of the drug resistance and side effects of current SERMs, the discovery of new SERMs is given more and more attention. Virtual screening is a validated method to high effectively to identify novel bioactive small molecules. Ligand-based machine learning methods and structure-based molecular docking were first performed for identification of ERα antagonist from in-house natural product library. Naive Bayesian and recursive partitioning models with two kinds of descriptors were built and validated based on training set, test set, and external test set and then were utilized for distinction of active and inactive compounds. Totally, 162 compounds were predicted as ER antagonists and were further evaluated by molecular docking. According to docking score, we selected 8 representative compounds for both ERα competitor assay and luciferase reporter gene assay. Genistein, daidzein, phloretin, ellagic acid, ursolic acid, (−)-epigallocatechin-3-gallate, kaempferol, and naringenin exhibited different levels for antagonistic activity against ERα. These studies validated the feasibility of machine learning methods for predicting bioactivities of ligands and provided better insight into the natural products acting as estrogen receptor modulator, which are important lead compounds for future new drug design.

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