Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
View Article and Find Full Text PDFTemporomandibular disorders (TMD) are a public health problem that affects around 12% of the global population. The treatment is based on analgesics, non-steroidal anti-inflammatory, corticosteroids, anticonvulsants, or arthrocentesis associated with hyaluronic acid-based viscosupplementation. However, the use of hyaluronic acid alone in viscosupplementation does not seem to be enough to regulate the intra-articular inflammatory process.
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