Measuring drug similarity using drug–drug interactions

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug interactions.Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action.In this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction networks.We explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning approaches.In unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group interactions.In addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug interactions.Our method exceeds existing approaches in terms of performance.Overall,our experiments demonstrate the effectiveness and practicability of the proposed similarity measure.On the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown MoA.On the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

PIMD:An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion

The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development.However,the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge.Here,we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning.In PIMD,drug similarity networks(DSNs)based on chemical,pharmacological,and clinical data are fused into an integrated DSN(iDSN)composed of many clusters.Rather than simple fusion,PIMD offers a systematic way to annotate clusters.Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses.PIMD provides new insights into the universality,individuality,and complementarity of different drug properties by evaluating the contribution of each property data.To test the performance of PIMD,we use chemical,pharmacological,and clinical properties to generate an iDSN.Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs.Within the top 20 recommended drug pairs,7 drugs have been reported to be repurposed.The source code for PIMD is available at https://github.com/Sepstar/PIMD/.