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Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.