Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network

Abstract

Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.

Towards sustainable production and utilization of plant-biomass-based nanomaterials: a review and analysis of recent developments

“…Final particle size and polydispersity depend upon the choice of lignin-dissolving solvent system. Dissolving lignin in a acetone:water mixture results in particles around 100 nm in diameter with narrow size range [ 61 , 62 ]. In contrast, the use of THF [ 52 ] or THF:water:ethanol [ 63 ] solvents results in particles around 200 to 300 nm and slightly higher polydispersity.…”

“…-25 mV pH 3.9 ζ ca. -27 mV Nanocomposites Pickering emulsions, drug delivery [ 61 , 62 ] KL Spherical, ca. 244 nm ζ ca.…”

“…Surface functionality of LNPs can further facilitate the development of novel LNP-based products in different new markets for lignin valorization. Examples of these new products are particles for drug delivery [ 62 , 87 ], enzyme immobilization [ 83 ], or sun screen applications [ 56 , 250 ], and their dispersing ability and UV-protective properties make LNPs attractive for paints, coatings, and cosmetics. In many applications, production of LNPs with visually appealing color, unlike the dark brown color of commercial technical lignin, are favorable.…”

Meta-Path Based Gene Ontology Profiles for Predicting Drug-Disease Associations

“…In general, only confirmed or positive drugdisease associations are available. There is no any validated negative drug-disease association or drug-disease nonassociation pair due to lack of its application value [25]. Under this condition, we consider our known drug-disease associations as positive samples whereas the remaining drug-disease pairs, apart from the positive pairs, are unlabeled samples.…”

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

“…Zeng et al developed a network-based deep learning approach, termed deepDR ( Zeng et al, 2019) , for in silico drug repurposing. Li et al used molecular structures and clinical symptoms via a deep convolutional neural network to identify drug–disease associations ( Li Z et al, 2019) . A network embedding method called NEDD ( Zhou et al, 2020 ) was proposed to predict novel associations between drugs and diseases using meta paths of different lengths.…”