DNA-encoded library (DEL) technology is valued for its high throughput, ease of operation, minimal sample requirements, low screening costs, and rapid processes. It has been employed to discover new inhibitors targeting various therapeutic proteins. This makes it a practical choice for initial discovery of active substances in pharmaceutical industry. 

However, the technology has certain limitations and faces challenges including expanding its applications beyond "affinity screening" to incorporate functional screening and addressing issues such as false positive signals during data analysis. Moreover, constructing high-quality compound libraries remains a critical task. Artificial intelligence (AI) demonstrates unique advantages in enhancing the potential of DEL technology.

In a study published in Journal of Medicinal Chemistry, the research groups led by Prof. LU Xiaojie and Prof. ZHENG Mingyue from the Shanghai Institute of Materia Medica of the Chinese Academy of Sciences, proposed an innovative approach to enhance the chemical space diversity of DEL compound libraries by integrating affinity screening with photo-crosslinking screening, and they integrated DEL technology with AI to accelerate drug discovery.

Researchers utilized DEL screening data for three proteins—BRD4, p300, and WDR5, and explored various aspects, including complementary chemical space of datasets, selection of training models, and development of combined and synergistic models. Combining DEL data with complementary chemical spaces, they constructed small-molecule predictive models targeting specific proteins, and identified the lead compounds for BRD4 and p300. These findings offered a new way for drug discovery targeting underexplored proteins.

Rather than aiming for predictive models to directly identify nanomolar-level active molecules, this approach provided a novel perspective for medicinal chemistry optimization, which is particularly valuable when active compounds identified through DEL screening face challenges in further optimization. It offers opportunities to refine and develop potential drug candidates. 

The integration of DEL and AI provides new research directions for drug discovery, and provides new tools to tackle complex therapeutic challenges.