The HDAC1 Predictor application provides an alternative method for assessing the potential of chemicals to be Histone deacetylas 1 (HDAC1) inhibitors. Compound is classified as active if the predicted IC50 value is lower than mean IC50 value of the reference drug Vorinostat (11.08 nM) otherwise compound is labeled as inactive. This application makes predictions based on Quantitative Structure-Activity Relationship (QSAR) models build on curated datasets generated from scientific articles. The consensus models were developed using open-source chemical descriptors based on ECFP4-like Morgan fingerprints and 2D RDKit descriptors, along with the random forest (RF), gradient boosting (GBM), support vector machines (SVM) algorithms, using Python 3.7. The models were generated applying the best practices for QSAR model development and validation widely accepted by the community. The applicability domain (AD) of the models was calculated as Dcutoff = ⟨D⟩ + Zs, where «Z» is a similarity threshold parameter defined by a user (0.5 in this study) and «⟨D⟩» and «s» are the average and standard deviation, respectively, of all Euclidian distances in the multidimensional descriptor space between each compound and its nearest neighbors for all compounds in the training set.