Life science organizations have long been using digital technologies, advanced analytics and artificial intelligence in their R&D efforts. These trends are certainly not new to the sector but COVID-19 has accelerated their pace of adoption.

Improved data science and digitization has the potential to compress drug development timelines and regulatory processes, improve the accuracy and pace of data collection, and deepen insights. AI has helped drive higher levels of precision and speed to otherwise complicated and time-consuming discoveries, particularly critical when the industry was pressed with the need to rapidly find treatments and vaccines for COVID-19, The pandemic helped highlight the pitfalls of the traditional new chemical entity process that usually takes many years, is costly and breeds a massive failure rate.

AI applications in biotech include drug target identification, drug and image screening, predictive modeling, clinical trials support and scientific literature mining. With AI, billions of molecules can be screened in weeks and vast libraries of chemical compounds can be more quickly combined. AI normalizes data from various platforms, curates unstructured data types and analyzes dimensions of data that are far beyond the capability of the basic human-computer tandem.