Welcome to our thought leadership series. We are pleased to introduce Petrina Kamya, VP and Global Head of AI Platforms at Insilico Medicine, and President of Insilico Medicine Canada. She oversees the company’s end-to-end generative AI drug discovery platform.
Insilico Medicine operates across two connected areas: developing advanced AI technologies and building a pipeline of drug candidates. Kamya’s team focuses on creating robust, scalable platforms that support multiple stages of drug discovery, from target identification and molecular design to clinical development strategy.
According to Kamya, generative AI is fundamentally changing how researchers understand disease and design new medicines. In target discovery, AI platforms can analyse large volumes of multimodal data, including scientific literature, genetics, RNA, tissue and cellular data. This provides researchers with a more complete view of the biological mechanisms driving disease and may improve the likelihood of success in later-stage clinical trials.
Generative AI is also accelerating molecular design. Traditionally, scientists optimised drug molecules through a lengthy, iterative process that could require synthesising and testing tens of thousands of compounds. AI-based platforms can optimise multiple properties simultaneously, potentially reducing the number of molecules that need to be produced and tested to only hundreds.
For Kamya, the strongest evidence of AI’s value is clinical validation. An AI-discovered drug candidate must demonstrate that it can reach clinical trials, meet safety requirements and ultimately show efficacy in patients. Real-world clinical progress is therefore essential to proving that AI can deliver more than faster preclinical research.
Building trust also requires transparency. Pharma companies, investors and regulators need to understand how AI systems work, how their results have been validated and where the technology should be integrated. As interest in AI grows, organisations must look beyond the fear of missing out and carefully assess which platforms, tools and partners have credible evidence behind them.
Looking ahead, Kamya believes AI could create value across the entire drug-development chain. Although clinical timelines cannot always be shortened, AI can improve patient recruitment, identify suitable patient populations, select promising trial locations and support better decision-making. The opportunity is not limited to one stage: AI has the potential to improve the speed, cost and probability of success throughout drug discovery and development.
