QSAR Modeling of JAK Inhibitors: Progress, Challenges, and Future Directions.

Dysregulated JAK-STAT signaling contributes to cancer and autoimmune diseases, positioning JAK proteins as key therapeutic targets. Small-molecule JAK inhibitors are therefore actively investigated, with quantitative structure-activity relationship (QSAR) modeling offering an in-silico framework to guide their design. This review evaluates 12 QSAR studies focused on JAK inhibition, emphasizing both 2D and 3D methodologies. Studies were retrieved from a PubMed search (“JAK QSAR”) and screened for relevance. Most 3D-QSAR models demonstrated strong internal robustness (IQR r2 = 0.91-0.97) but weaker predictive performance (IQR q2 and r2pred = 0.53-0.69), indicating overfitting. This likely reflects limited external validation: only one study employed an external test set. Therefore, more rigorous validation is needed to ensure that novel predictions translate to real-world applicability. Conversely, contour map analyses yielded interpretable structural insights, informing the design of novel analogues with predicted potencies exceeding dataset leads. However, these predictions were not experimentally confirmed, further limiting translational relevance. Methodologically, CoMFA and CoMSIA both demonstrated similar internal robustness, as did binding site-based and atom overlap-based superimposition. This finding suggests that both modeling and alignment strategies can be employed in parallel to enhance interpretability without compromising model fit. 2D-QSAR studies employing Multiple Linear Regression (MLR) identified compact, predictive descriptor sets that simplified model construction. Collectively, the findings recommend that future studies (i) incorporate external validation more routinely, (ii) apply both modeling approaches and alignment strategies in parallel, (iii) cross-check results with complementary in silico methods, (iv) apply MLR to streamline descriptor sets, and (v) confirm predicted inhibitors with biological assays.