3CLpro inhibitors: DEL-based molecular generation

Abstract

Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

“…Several cases of AI based on an in vitro library have been reported. Xiong’s team constructed a database based on DEL and implemented high-affinity molecule prediction and generation through AI modeling. , Lim et al combined DEL and ML for the effective screening of carbonic anhydrase IX (CAIX), soluble epoxide hydrolase (sEH), and sirtuin 2 (SIRT2). These studies suggest that combining library-based in vitro selection strategies with AI has enormous potential in drug discovery …”