Computer-aided drug design (CADD) plays as a powerful tool for rapidly and accurately screening of several million compounds for potential inhibitors of enzymes . The adoption of CADD methods is rapidly increased due to their potential to significantly reduce the cost and time of a new drug development. CADD can be used in both purposes including searching for new inhibitors and repurposing for existing drugs. CADD has been donating to the discovery of severally available drugs such as dorzolamide, 26-27 saquinavir, ritonavir, and indinavir. 21 In this work, we aim to use a combination of computational and experimental approaches to find potential inhibitors for inhibiting neuraminidase. In particular, the trained machine learning (ML) model was employed to predict the ligand-binding affinity of ca. 2 million compounds of ChEMBL database to neuraminidase. The experimental studies were then carried out to validate the ML outcomes. The shortlist of potential candidates were obtained. The experiment would be then validated the ML outcomes. Besides, molecular docking and MD simulations were used to clarify the physical insights into the binding process of these compounds to neuraminidase.