Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins

Abstract

Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study aims to predict unknown ligand–target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs. We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep learning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datasets for more interpretability, high throughput, and broad applicability. Graphical abstract

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

“…For structure-based screening, quantitative structure–activity relationships (SARs) can be predicted with or without employing a tertiary structure, which, until recently, was largely confined to experimentally resolved structures, but now, with advancements like the AlphaFold and RoseTTAFold algorithms, has seen expanded possibilities. Instead of tertiary structures, pair-based screening involves training models using primary representations of proteins in the form of SMILES as input, coupled with biochemical activity data, which benefit from being more deployable compared to complex-based screening [ 81 ]. Specifically, training billions of compounds using deep CNNs based on SMILES representation is significantly less computationally expensive relative to physics-based 3D docking methods.…”

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug–Target Affinity Prediction

“…While binding pockets significantly influence affinity, few approaches effectively integrate pocket information into drug–target affinity (DTA) prediction. DeepPS, for instance, explicitly leverages functional motifs extracted from protein amino acid sequences [ 20 ]. Furthermore, TANKBind [ 8 ] utilizes P2Rank as a preprocessing step to refine the interaction scope.…”