We are among the few labs with deep expertise in the physics underlying mass spectrometry instruments. This allows us to optimize instrument parameters, reduce systematic errors, and enhance signal fidelity. By leveraging a physics-driven understanding of ion behavior, detector response, and source dynamics, we ensure highly reproducible data and maximize the reliability of our experimental outputs.

Our platform integrates AI-powered analytics to extract patterns, predict compound behavior, design synthetic combinatorial libraries, and guide experimental decisions with speed and precision. Using machine learning models trained on high-resolution mass spectrometry data, we can prioritize hits, identify off-target effects, and uncover structure–activity relationships. This accelerates the drug discovery process by reducing false positives, streamlining validation, and enabling data-driven hypothesis generation at scale. AI is not an add-on; rather it is embedded at every stage to enhance automation, accuracy, efficiency, and insight.

We work with medicinal natural sources from diverse regions around the world to broaden the chemical space and increase the likelihood of successful drug identification. To support this, we are developing a natural language processing (NLP)-driven knowledge graph that connects traditional medicine knowledge such as indications, symptoms, and applications with modern scientific data. This system will help us extract insights from unstructured texts from many cultures across the globe, identify overlooked therapeutic links, and guide data-driven decisions as we expand our drug discovery pipelines.