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Drug-protein binding affinity is a critical component of drug efficacy and safety. However, experimental techniques to measure drug-protein binding affinity are either laborious or time-consuming. In this study, we developed a novel computational method based on the sequence of a protein to predict binding affinities of small molecules. Sequence-based models were constructed using the chemical features, physicochemical properties, and side-chain conformations of drug candidates in a training dataset. In addition, sequence and structure-based models were developed to enhance the prediction performance. These models, were able to effectively discriminate drugs with low binding affinities from others. Further, a random forest based classifier was developed to combine the sequence- and structure-based models and correctly predict drug-protein binding affinity. This classifier, consisting of only one model, was able to improve the classification accuracy to 80.8% using be359ba680
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