CDTA: Consistency-based deep learning method for drug-target affinity prediction using sequential features from protein language model

Accurate drug-target affinity prediction is a critical phase for accelerating and reducing the failure rate of the drug discovery process. It is more challenging in the absence of reliable 3D structural information. Even though protein 3D structure prediction methods have improved, they still lack reliability and generalisability. Ensemble methods attempt to address these shortcomings, but these techniques themselves lack consistency in the values across different models. Therefore, we introduce a novel consistency-based deep learning method named CDTA using sequential features, which consists of a main prediction module and a supporting surrogate module, and the outputs of both modules are incorporated to improve the prediction accuracy. The method employs a novel feature combination, integrating explicit and deep learning-derived features from a protein language model in the prediction module. The CDTA achieves a Pearson correlation coefficient above 0.86 and a Concordance index (CI) over 0.87 on benchmark datasets KIBA, Davis, and BindingDB. Further, it surpasses the current state-of-the-art methods in the accuracy metric r m 2 across all three datasets. Lightweight and competent, CDTA does not rely on 3D structural information. Its simple architecture enables processing of larger datasets on limited hardware without compromising accuracy. Moreover, it facilitates not only the early identification of potential drug candidates but also helps to minimise the failure rate of drug discovery. Ultimately, it accelerates the process and reduces associated costs.