Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning..

Background: Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning. Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning. Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP. Results: The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively. Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

ABCnet : Self-Attention based Atom, Bond Message Passing Network for Predicting Drug-Target Interaction

Drug-target interaction (DTI) is a methodology for predicting the binding affinity between a compound and a target protein, and a key technology in the derivation of candidate substances in drug discovery. As DTI experiments have progressed for a long time, a substantial volume of chemical, biomedical, and pharmaceutical data have accumulated. This accumulation of data has occurred contemporaneously with the advent of the field of big data, and data-based machine learning methods could significantly reduce the time and cost of drug development. In particular, the deep learning method shows potential when applied to the fields of vision and speech recognition, and studies to apply deep learning to various other fields have emerged. Research applying deep learning is underway in drug development, and among various deep learning models, a graph-based model that can effectively learn molecular structures has received more attention as the SOTA in experimental results were achieved. Our study focused on molecular structure information among graph-based models in message passing neural networks. In this paper, we propose a self-attention-based bond and atom message passing neural network which predicts DTI by extracting molecular features through a graph model using an attention mechanism. Model validation experiments were performed after defining binding affinity as a regression and classification problem: binary classification to predict the presence or absence of binding to the drug-target, and regression to predict binding affinity to the drug-target. Classification was performed with BindingDB, and regression was performed with the DAVIS dataset. In the classification problem, ABCnet showed higher performance than MPNN, as it does in the existing study, and in regression, the potential of ABCnet was checked compared to that of SOTA. Experiments indicated that in binary classification, ABCnet has an average performance improvement of 1% than other MPNN on the DTI task, and in regression, ABCnet has CI and performance degradation between 0.01 and 0.02 compared to SOTA.

AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites

Investigating drug-target interactions plays a critical role in drug design and discovery. The vast chemical and proteomic space, along with the cost associated with invirto experiments motivate the use of computational methods to narrow down the search space for novel interaction of drug target pairs. Among all computational methods, deep learning algorithms have gained increased attention due to their power in automatically learning and extracting feature representations, and therefore identifying, processing and extrapolating complex hidden interactions between drugs and targets. In this study, we introduce and implement a new graph-based prediction model called AttentionSiteDTI. Our proposed model utilize the binding sites (pockets) of the proteins as the input for the target protein, and it uses a self-attention mechanism to make the model learn which binding sites of the protein interact with a given ligand. This, indeed, complements the black-box nature of deep learning-based methods and enables interpretability, while achieving state of the art results in drug target interaction prediction task on three datasets. The AttentionSite DTI achieves AUC of 0.97 (for seen proteins), 0.94 (for unseen proteins) in the customized BindingDB dataset, 0.971 in the DUD-E dataset, and 0.991 in the human dataset. In general, the prediction results on these datasets show the superiority of our AttentionSiteDTI compared to previous graph-based models, and our ablation studies proves the effectiveness of our proposed model in prediction of drug-target interactions. In addition, through multidisciplinary collaboration in this work, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, and then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our AttentionSiteDTI as effective pre-screening tool in drug repurposing applications.