The increasing complexity of cancer biology and the expanding availability of clinical and multi-omics data have created new opportunities to refine patient stratification and extend the therapeutic potential of existing treatments. Realizing this potential, however, requires computational approaches that can integrate diverse datasets while preserving biological relevance. We present an integrated in-silico framework that transforms heterogeneous multimodal datasets into standardized, analysis-ready resources through systematic curation, harmonization, quality control, and data integration, providing a reliable foundation for downstream analyses.

Machine learning and statistical approaches are then applied to identify predictive and prognostic biomarker signatures associated with treatment response and clinical outcome. These signatures enable biologically interpretable patient stratification, distinguish likely responders from non-responders, and provide insight into molecular mechanisms underlying therapeutic response and resistance.

Biomarkers validated within a specific disease indication are subsequently evaluated across other diseases that share similar molecular characteristics, providing an evidence-based strategy for identifying additional patient populations that may benefit from existing therapies. Complementary analyses, including target identification, in-silico perturbation studies, and drug repurposing assessments, further place these findings within a mechanistic context and support the generation of testable therapeutic hypotheses.

By integrating data-driven biomarker discovery with biological interpretation, this framework provides a systematic approach to patient stratification and indication expansion, supporting more informed therapeutic decision-making and advancing the application of precision oncology across diverse disease settings.

Synthetic route design is a persistent bottleneck in molecular R&D. Traditional route planning depends strongly on individual chemist experience, which can restrict route diversity, prolong development cycles, and increase experimental cost. This limitation is becoming more critical as pharmaceutical and materials research demand faster molecular iteration.
Here we present ChemAIRS, an intelligent synthesis design platform that integrates large-scale chemical data mining and deep learning to generate diverse and feasible synthetic routes within minutes. Beyond route generation, the platform also supports reaction impurity prediction, process optimization, and reaction condition recommendation, providing chemists with a comprehensive workflow for synthesis design and decision-making in both medicinal chemistry and process chemistry. More recently, we developed a new framework to involve LLM in ChemAIRS to improve the accuracy and interpretability in both route design and impurity prediction.
ChemAIRS reduces dependence on individual expertise, improves synthesis efficiency, and accelerates the translation of molecular ideas into practical applications. More broadly, it offers a digital and intelligent framework for modern synthetic chemistry research.