IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques

Inverse Virtual Screening is a powerful technique in the early stage of drug discovery process. This technique can provide important clues for biologically active molecules, which is useful in the following researches of durg discovery. In this work, combining with Word2vec, a natural language processing technique, dense fully connected neural network (DFCNN) algorithm is utilized to build up a prediction model. This model is able to perform a binary classification. Based on the query molecule, the input protein candidates can be classified into two subsets. One set is that potential targets with high possibilities to bind with the query molecule and the other one is that the proteins with low possibilities to bind with the query molecule. This model is named as IVS2vec. IVS2vec also can output a score reflecting binding possibility of the association between a protein and a molecule, which is useful to improve efficiency of research. We applied IVS2vec on several databases related to drug development and shown that our model can detect possible therapeutic targets. In addition, our model can identify targets related to adverse drug reactions which is useful to improve medication safety and repurpose drugs. Moreover, IVS2vec can give a very fast speed to perform prediction jobs. It is suitable for processing a large number of compounds in the chemical databases. We also find that IVS2vec has potential capabilities and outperform other state-of-the-art docking tools such as Autodock vina. In this study, IVS2vec brings many convincing results than Autodock vina in the reverse target searching case of Quercetin.