DTBAPred: Improved Prediction Drug-Target Binding Affinity using Machine Learning Approach

Abstract

Drugs are small molecules that usually bind with proteins, also called target, to control the cellular process of target in combatting disease associated with target (s). Effectiveness of a drug hugely depends upon the strength of its binding affinity with its partner proteins. As drug discovery is a lengthy and expensive process, in silico drug discovery and drug repurposing is an alternative complementary avenue for the researchers. Nowadays drug-target binding affinity (DTBA) prediction is a part and parcel of in any in silico drug discovery and drug repurposing process. There exist many precedents in the literature which considered machine learning (ML) based approach to predict DTBA. In the present article, we proposed a novel combination of features to represent drugs, targets, and feed into ML model to predict the DTBA. The proposed CatBoost based model DTBAPred outperformed the state-of-the-art traditional ML based methods in DAVIS benchmark dataset with 0.276 MSE, 0.579 R-square, and 0.866 CI. We considered two different fingerprints i.e., SMILES and Morgan to represent drugs; and SMILES fingerprint-based model showed better performance than Morgan fingerprint-based model, emphasizing that different fingerprints may also impact the DTBA prediction results. In summary obtained results indicate the superiority of the proposed method over existing traditional feature-based ML models for the same purpose and emphasized the incorporation of different fingerprints in the model. We believe, our proposed method will support to improve the DTBA prediction and escalate the drug discovery process.