To determine chemical-protein interactions (CPI) is costly, time-consuming, and labor-intensive. In silico prediction of CPI can facilitate the target identification and drug discovery. Although many in silico target prediction tools have been developed, few of them could predict active molecules against multitarget for a single disease. In this investigation, naive Bayesian (NB) and recursive partitioning (RP) algorithms were applied to construct classifiers for predicting the active molecules against 25 key targets toward Alzheimer's disease (AD) using the multitarget-quantitative structure-activity relationships (mt-QSAR) method. Each molecule was initially represented with two kinds of fingerprint descriptors (ECFP6 and MACCS). One hundred classifiers were constructed, and their performance was evaluated and verified with internally 5-fold cross-validation and external test set validation. The range of the area under the receiver operating characteristic curve (ROC) for the test sets was from 0.741 to 1.0, with an average of 0.965. In addition, the important fragments for multitarget against AD given by NB classifiers were also analyzed. Finally, the validated models were employed to systematically predict the potential targets for six approved anti-AD drugs and 19 known active compounds related to AD. The prediction results were confirmed by reported bioactivity data and our in vitro experimental validation, resulting in several multitarget-directed ligands (MTDLs) against AD, including seven acetylcholinesterase (AChE) inhibitors ranging from 0.442 to 72.26 μM and four histamine receptor 3 (H3R) antagonists ranging from 0.308 to 58.6 μM. To be exciting, the best MTDL DL0410 was identified as an dual cholinesterase inhibitor with IC50 values of 0.442 μM (AChE) and 3.57 μM (BuChE) as well as a H3R antagonist with an IC50 of 0.308 μM. This investigation is the first report using mt-QASR approach to predict chemical-protein interaction for a single disease and discovering highly potent MTDLs. This protocol may be useful for in silico multitarget prediction of other diseases.

The presence of cancer stem cells (CSC), which possess the ability of self-renewal and cancer initiation, is correlated with poor prognosis and drug resistance of breast cancer patients. But the molecular regulatory networks for maintenance of CSC function still remain unclear. Here, we identified that an estrogen-inducible gene , whose expression is significantly upregulated in ER breast CSCs, is a critical player for regulating ER breast CSC function. FXYD3 amplification is crucial in mediating tamoxifen resistance in ER breast cancer cells. Interestingly, we also find that stem cell-related transcription factor SOX9 directly promotes FXYD3 expression, and FXYD3 is indispensable for SOX9 nucleus localization, thus forming a positive regulatory feedback loop for FXYD3 amplification and function. In terms of mechanism, FXYD3 interacts with Src and ERα to form an activated complex and triggers Src to transduce nongenomic estrogen signaling for facilitating ER breast CSCs. Collectively, these results establish a critical role for SOX9/FXYD3/Src axis in boosting nongenomic estrogen signaling and SOX9 nucleus entry, which is required for maintenance of ER breast CSCs and endocrine resistance. Targeting FXYD3-mediated pathway might be a promising therapeutic strategy for hormone therapy-refractory ER breast cancer. SOX9/FXYD3/Src axis is critical for promoting CSC function and tamoxifen resistance in ER breast cancer.

The classification models were constructed to discover neuroprotective compounds against glutamate or H2O2-induced neurotoxicity through machine learning approaches.

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