Prediction of Spheroid Cell Death Using Fluorescence Staining and Convolutional Neural Networks.

Three-dimensional (3D) cell culture is emerging for drug design and drug screening. Skin toxicity is one of the most important assays for determining the toxicity of a compound before being used in skin application. Much work has been done to find an alternative assay without animal experiments. 3D cell culture is one of the methods that provides clinically relevant models with superior clinical translation compared to that of 2D cell culture. In this study, we developed a spheroid toxicity assay using keratinocyte HaCaT cells with propidium iodide and calcein AM. We also applied the transfer learning-containing convolutional neural network (CNN) to further determine spheroid cell death with fluorescence labeling. Our result shows that the morphologies of the spheroid are the key features in determining the apoptosis cell death of the HaCaT spheroid. Our CNN model provided good statistical measurement in terms of accuracy, precision, and recall in both validation and external test data sets. One can predict keratinocyte spheroid cell death if that spheroid image contains the fluorescence signals from propidium iodide and calcein AM. The CNN model can be accessed in the web application at https://qsarlabs.com/#spheroiddeath.

Prediction of KRASG12C inhibitors using conjoint fingerprint and machine learning-based QSAR models.

Kirsten rat sarcoma virus G12C (KRASG12C) is the major protein mutation associated with non-small cell lung cancer (NSCLC) severity. Inhibiting KRASG12C is therefore one of the key therapeutic strategies for NSCLC patients. In this paper, a cost-effective data driven drug design employing machine learning-based quantitative structure-activity relationship (QSAR) analysis was built for predicting ligand affinities against KRASG12C protein. A curated and non-redundant dataset of 1033 compounds with KRASG12C inhibitory activity (pIC50) was used to build and test the models. The PubChem fingerprint, Substructure fingerprint, Substructure fingerprint count, and the conjoint fingerprint-a combination of PubChem fingerprint and Substructure fingerprint count-were used to train the models. Using comprehensive validation methods and various machine learning algorithms, the results clearly showed that the XGBoost regression (XGBoost) achieved the highest performance in term of goodness of fit, predictivity, generalizability and model robustness (R2 = 0.81, Q2CV = 0.60, Q2Ext = 0.62, R2 - Q2Ext = 0.19, R2Y-Random = 0.31 ± 0.03, Q2Y-Random = -0.09 ± 0.04). The top 13 molecular fingerprints that correlated with the predicted pIC50 values were SubFPC274 (aromatic atoms), SubFPC307 (number of chiral-centers), PubChemFP37 (≥1 Chlorine), SubFPC18 (Number of alkylarylethers), SubFPC1 (number of primary carbons), SubFPC300 (number of 1,3-tautomerizables), PubChemFP621 (N-C:C:C:N structure), PubChemFP23 (≥1 Fluorine), SubFPC2 (number of secondary carbons), SubFPC295 (number of C-ONS bonds), PubChemFP199 (≥4 6-membered rings), PubChemFP180 (≥1 nitrogen-containing 6-membered ring), and SubFPC180 (number of tertiary amine). These molecular fingerprints were virtualized and validated using molecular docking experiments. In conclusion, this conjoint fingerprint and XGBoost-QSAR model demonstrated to be useful as a high-throughput screening tool for KRASG12C inhibitor identification and drug design.