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Updated: Jul 7, 2026
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Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines
Published on: October 8, 2015
Rational Inhibitor Discovery for BRAFV600E Using PSeMut, a Sequence-Driven Model.
Summary
We developed PSeMut, a structure-free computational model, to predict how mutations affect drug efficacy. This approach successfully identified a novel drug candidate, SNS-314, demonstrating potent and mutation-selective activity against BRAF-driven cancers.
Area of Science:
- Computational chemistry
- Drug discovery
- Genomics
Background:
- Mutations in drug targets reduce small-molecule drug effectiveness and drive disease progression.
- Current methods for discovering mutation-tolerant drugs are not easily scalable for high-throughput screening.
- There is a need for efficient computational tools to design drugs that maintain potency against mutated targets.
Purpose of the Study:
- To introduce PSeMut, a structure-free Siamese model for predicting mutation-induced changes in drug activity.
- To evaluate PSeMut's performance against established methods using a variant-resolved benchmark.
- To demonstrate the utility of PSeMut within a structure-free drug discovery pipeline.
Main Methods:
- Developed PSeMut, a structure-free Siamese model utilizing PSICHIC-derived protein-ligand fingerprints to predict activity changes.
- Benchmarked PSeMut against classical models on variant-resolved data, assessing performance via RMSE.
- Implemented a structure-free prioritization pipeline integrating scaffold novelty, activity scoring, mutation tolerance ranking, and clustering for experimental validation.
Main Results:
- PSeMut achieved a test RMSE of 0.400 ± 0.025, outperforming classical baselines.
- Removing the exchange-consistency constraint in PSeMut led to decreased performance.
- The PSeMut-integrated pipeline successfully prioritized SNS-314, which exhibited mutation-selective cellular activity and suppressed tumor growth in vivo.
Conclusions:
- PSeMut is an effective tool for predicting mutation-induced activity changes in a structure-free manner.
- The developed screening workflow links sequence-based modeling to experimental validation for rational drug design.
- PSeMut enables the prioritization of mutation-resilient drug scaffolds for various diseases.
