Rational Inhibitor Discovery for BRAF V600E Using PSeMut , a Sequence‐Driven Model

ABSTRACT Mutational heterogeneity at drug targets both undermines small‐molecule efficacy and contributes to disease. A practical response is to discover scaffolds that maintain potency in the presence of mutations; however, prevailing biological research strategies are not readily scalable, constraining high‐throughput design and discovery. We present PSeMut, a structure‐free Siamese model that contrasts wild‐type and mutant protein‐ligand fingerprints (PSICHIC‐derived) to predict mutation‐induced activity changes. On a variant‐resolved benchmark, PSeMut attains a test RMSE of 0.400 ± 0.025 and consistently outperforms classical baselines trained on identical features and splits; removing the exchange‐consistency constraint degrades performance. To evaluate the feasibility of the framework in a prospective setting, we applied a structure‐free prioritization pipeline—combining scaffold‐novelty filtering, PSICHIC activity scoring, PSeMut‐based mutation‐tolerance ranking, clustering‐based selection, and biological validation. This workflow prioritized SNS‐314 for follow‐up testing. In the selected validation assays, SNS‐314 showed mutation‐selective cellular activity (IC50 = 0.45 μM in BRAF V600E ; 7.5‐fold vs. BRAF WT ), suppressed the BRAF‐MEK–ERK signaling axis, and produced about 50% tumor growth inhibition in vivo without overt toxicity. Together, these results validate the effectiveness of PSeMut in a structure‐free, mutation‐aware screening workflow that links sequence‐driven modeling to experimental confirmation and enables rational prioritization of mutation‐resilient scaffolds across heterogeneous disease settings.