BoltzOmics: Predicting genetic variant effects on drug binding with Boltz-2
Khoa Ngo1,2, Kermit L Carraway1,3, Colleen E Clancy1,2,4
1Center for Precision Medicine and Data Sciences, School of Medicine, University of California, Sacramento, CA 95817, USA.
Iscience
|July 26, 2026
Summary
BoltzOmics rapidly predicts how genetic variants affect drug binding using AI. This accelerates drug discovery and precision medicine by assessing mutation-drug interactions computationally.
Area of Science:
- Biochemistry
- Computational Biology
- Pharmacogenomics
Background:
- Understanding genetic variant effects on drug-receptor binding is crucial for precision medicine and drug development.
- Experimental methods for mutation-drug profiling are time-consuming and costly.
- Current computational methods often compromise accuracy for scalability.
Purpose of the Study:
- To develop BoltzOmics, an interactive, open-source platform integrating a deep learning model (Boltz-2) for rapid assessment of mutation effects on drug binding.
- To provide a scalable, high-throughput computational framework for analyzing drug-variant interactions.
Main Methods:
- BoltzOmics integrates Boltz-2 for biomolecular structure prediction.
- The workflow starts with amino acid sequences to query genetic variant databases.
- It generates wild-type and mutant protein structures to predict drug binding affinity changes.
Main Results:
- BoltzOmics achieved high correlations with experimental drug IC50 data (up to 0.76 for wild-type, 0.60 for mutants) across four targets (hERG, NaV1.5, HER2, CYP3A4).
- The platform enables rapid, scalable assessment of mutation effects on drug binding.
Conclusions:
- BoltzOmics offers a practical AI-driven framework for accelerating computational drug discovery.
- The platform advances precision medicine research by enabling efficient analysis of drug-variant interactions.
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