AI-guided De Novo Design of DHPS Inhibitors

Hypusine modification of eIF5A by Deoxyhypusine Synthase (DHPS) is essential for the replication of a broad range of viruses, making DHPS an attractive host-directed therapy (HDT) target for antiviral drug discovery. AI-based drug design methods spanning binding affinity prediction and de novo molecular generation are rapidly proliferating, yet systematic benchmarking of diverse algorithmic paradigms under identical conditions on a shared target, with experimental validation, remains scarce. Here, we present an integrated AI benchmarking pipeline targeting DHPS and evaluate the full process from computational prediction to experimental validation. We compared 13 MM-GBSA score estimation models and 10 de novo molecular generation methods under 5-seed repeated experiments. For the prediction task, we propose an Out-of-Fold stacking ensemble integrating gradient boosting, graph neural networks, and chemical language models, achieving the highest Spearman rank correlation of 0.861 among all compared models. For the generation task, we propose RL-Design, a reinforcement learning-based framework using the ensemble predictor as a scoring oracle. RL-Design achieved the highest hit rate of 92.02