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Prediction of anti‐HIV activity on the basis of stacked auto‐encoder

The prediction of biologically active compounds plays a very important role for high‐throughput screening approaches in drug discovery. Most computational models, in this area, concentrate on measuring structural similarities between chemical elements. There are various methods to predict anti‐HIV activity, such as artificial neural network and support vector machine, but generally using shallow machine learning with low accuracies and less samples. In this work, one of deep learning methods, stacked auto‐encoder (SAE), is proposed to predict anti‐HIV activity of a broad group of compounds for the first time. Through contrasting experiments of artificial neural network, support vector machine, and SAE under the same condition, the accuracy after descriptors screening is higher than using raw descriptors, and SAE performs better than the other two methods to achieve the perfect forecast of anti‐HIV activity. It has a great significance on promoting anti‐HIV drug design, which therefore can reduce research and development costs and improve the efficiency of anti‐HIV drug discovery.