We are glad to present our outlook on ‘Artificial Intelligence in Target and Biomarker Discovery-2022’. The attached presentation outlines how AI can better elucidate disease biology and assist with building a robust hypothesis for target and biomarker identification.
Most clinical trials fail because of poor safety and efficacy resultant from a lack of holistic disease understanding. The traditional approaches are usually unidimensional and fail to account for the complex nature of biology. While we are observing an explosion of healthcare data, traditional methods are unable to address the challenges of analyzing the massive volumes and diverse data types. AI can help connect these multidimensional data silos into effective disease models.
The use of AI to mine and analyze medical big data – including scientific literature, omics datasets, and clinical trials data – is accelerating the identification of new drug targets, predicting drug response in patient sub-populations, identifying prognostic and diagnostic biomarkers, repurposing and designing optimal drug designs.
From a novel biological target discovery to therapy design for Wilson’s disease in 18 months, to the first-ever AI-designed drug to enter clinical trials for lung fibrosis, AI is proving its worth in every step of the value chain. With AI revolutionizing drug discovery and development, it would be timely to consider investing in AI.
In the past few years, several AI companies have spurted with a wide array of capabilities to answer similar questions. However, critical and careful considerations are required to distinguish the most optimum AI platform/solution among the crowd. Our deep understanding of the evolving AI space in the pharma sector allows us to identify the right strategic partners for you and guide you through the critical steps of the journey.
We would be delighted to share our insights to help you advance your strategies, and catalyze potential partnership and investment opportunities in this space.