A chemical language based approach for protein - ligand interaction prediction

Identification of high affinity drug-target interactions (DTI) is a major research question in drug discovery. In this study, we propose a novel methodology to predict drug-target binding affinity using only ligand SMILES information. We represent proteins using the word-embeddings of the SMILES representations of their strong binding ligands. Each SMILES is represented in the form of a set of chemical words and a protein is described by the set of chemical words with the highest Term Frequency- Inverse Document Frequency (TF-IDF) value. We then utilize the Support Vector Regression (SVR) algorithm to predict protein - drug binding affinities in the Davis and KIBA Kinase datasets. We also compared the performance of SMILES representation with the recently proposed DeepSMILES representation and found that using DeepSMILES yields better performance in the prediction task. Using only SMILESVec, which is a strictly string based representation of the proteins based on their interacting ligands, we were able to predict drug-target binding affinity as well as or better than the KronRLS or SimBoost models that utilize protein sequence.