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.

• Why are vendor and pharmaceutical industry partnerships focusing on the discovery of novel compounds for treatments related to oncology, neurological conditions, aging, and rare diseases poised for growth?
• Which top companies have successful clinical-stage molecules either in co-development/licensing agreements with pharma or developed in-house?
• What are the significant growth strategies that you can consider to gain a competitive advantage over industry leaders?