Drug Discovery

Projects bridges AI-driven target prioritization with precision experimental pipelines to uncover novel therapeutics. Leveraging structural bioinformatics and high-throughput virtual screening, the platform computationally evaluates millions of compounds against metagenes—AI-identified genes critical to disease mechanisms (e.g., cancer progression, antimicrobial resistance).

Meta-analysis of multi-omic datasets pinpoints these metagenes, while molecular docking and dynamics simulations rank compounds for binding affinity and specificity. Top candidates undergo rigorous, hypothesis-driven experimental validation, including functional assays and mechanistic studies, to confirm efficacy and safety. By prioritizing scalable computational workflows over resource-heavy trial-and-error, the project accelerates the translation of AI-derived insights into actionable leads, redefining efficiency in early-stage drug discovery for complex diseases.