Enhancing Chemical Prediction in Plasma Protein Binding: A Deep Learning Model Combining Molecular Descriptors and Fingerprint Representation With Attention Mechanisms.

Plasma protein binding (PPB) is an important pharmacokinetic parameter. It is important to measure the PPB properties of drug molecules during drug development. However, in vivo or in vitro measurements are time-consuming. Therefore, in silico prediction methods are promising time-saving alternatives. This study presents a new deep learning model called enhancing plasma protein binding prediction (ePPBP) that merges molecular descriptors, molecular fingerprints and graph features for predicting PPB. The ePPBP currently has state-of-the-art (SOTA) performance, with an Rp of 0.8663, an R of 0.8630, an MAE of 0.0613 and an RMSE of 0.1041 on the test set. In addition, an ablation experiment demonstrated that different molecular representations can improve ePPBP performance. Next, an uncertainty estimation experiment was used to estimate the confidence when ePPBP was used to predict unknown chemicals. The MHFP distance and RDKFP similarity were selected as confidence indicators to determine whether the predictions were credible from ePPBP.