Frost Radar: AI-Enabled Drug Discovery, 2025

A Benchmarking System to Spark Companies to Action - Innovation That Fuels New Deal Flow and Growth Pipelines

The discovery of new drug targets for diseases with unmet medical needs has long been hindered by the complexity of processing and interpreting massive volumes of biological data. The increasing volume and veracity of clinical and research data compels the use of enabling tools and technologies, such as cloud computing, AI, machine learning, natural language processing, and advanced analytics, to make the shift from the slow, traditional approach to drug discovery and development to one that is relatively fast, rational, and data-driven. To remain competitive, industry players need to establish the right balance of data, AI, and computational capability and match it with the wet lab capability. With drug discovery being primarily about understanding biological networks and drug-target interactions, AI has been seamlessly integrating itself to allow identification and prioritization of disease-specific unmet therapeutic targets. Vendor/pharmaceutical industry partnerships continue to grow, with a focus on discovery of novel compounds for treatments related to oncology, neurological conditions, aging, and rare diseases. Most established companies have successful clinical-stage molecules either in co-development/licensing agreements with pharma or developed in-house.

Frost & Sullivan analyzes numerous companies in an industry. Those selected for further analysis based on their leadership or other distinctions are benchmarked across 10 Growth and Innovation criteria to reveal their position on the Frost Radar. The publication presents competitive profiles of each company on the Frost Radar considering their strengths and the opportunities that best fit those strengths.