Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that the similarity matrices used in MF models mostly only describe structural similarity which does not entirely represent the similarity between drugs or targets. In this work, we have developed a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We have applied the proposed model on a drug-virus association dataset for COVID, and compared the performance with other existing MF models developed for this disease. The results show that the similarity fusion method can provide more information for drug-drug and virus-virus similarity, and hence improve the performance of MF models. Finally, we provide the top 10 drugs as prioritized by our proposed model, and discuss how they have been supported by literature.