Image credit- shutterstock
Hong Kong headquartered Insilico Medicine, a clinical-stage drug discovery and development company driven by generative artificial intelligence (AI), has announced a strategic research collaboration with ASKA Pharmaceutical, a specialised pharmaceutical company with a strong focus on internal medicine, obstetrics, and gynecology.
This partnership aims to identify novel therapeutic targets with high drug development potential for challenging gynecological conditions, including endometriosis, uterine fibroids, and adenomyosis, by leveraging Insilico’s proprietary AI-driven target identification engine, PandaOmics.
Gynecological diseases have long posed challenges, including difficult diagnosis, limited treatment options, and a substantial disease burden, affecting millions of patients worldwide. According to estimates from the World Health Organization, endometriosis affects approximately 190 million women globally, while uterine fibroids and adenomyosis impact an even larger demographic, often co-occurring and heavily impacting women's reproductive health.
This collaboration combines ASKA’s deep expertise in gynecological therapeutics with Insilico Medicine’s advanced AI-driven biological analytics capabilities to address major unmet medical needs affecting hundreds of millions of patients worldwide and to accelerate the development of innovative solutions. Under the partnership, Insilico will leverage PandaOmics to identify and explore disease hypotheses, while ASKA will validate the AI-predicted therapeutic targets with high translational potential.
The project will be led by the Target Discovery team with Dr Frank Pun, Head of Insilico Medicine’s Hong Kong site. The team recently unveiled Target Identification Pro (TargetPro), an AI-driven framework that accelerates therapeutic target discovery by integrating multi-modal data into disease-specific models. By identifying context-dependent predictive patterns, TargetPro outperforms existing target identification models in accurately predicting which targets are most likely to advance to clinical stages.
The framework goes beyond validating established clinical targets to nominate novel, high-potential candidates optimized for immediate preclinical validation.
