De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference

Abstract

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.

Mlp4green: A Binary Classification Approach Specifically for Green Odor

“…Our study created a map of the chemical space network (CSN) using the similarity between chemical fingerprints [ 28 , 29 ]. Every molecule has a specific position in a theoretical region called chemical space.…”

TCoCPIn: Unveiling Topological Characteristics of Chemical-Protein Interaction Networks for Discovering Novel Features in Interactions

“…Despite numerous studies on the interaction between chemical substances and proteins, current research still faces several thorny problems. First, due to different experimental conditions and technical means used in different studies, integrating and standardizing data are difficult, limiting the accuracy and repeatability of large-scale network analysis [7] [8] .Additionally, the chemical-protein interaction network is highly complex and heterogeneous, making it challenging for traditional analysis methods to fully reveal hidden associations and functional modules therein [9] [10]. In large-scale networks, identifying and verifying key nodes and pathways with biological significance remains a significant challenge, requiring the combination of multiple data sources and analysis methods [11].…”

“…By applying topological analysis, researchers can uncover the global structure and local characteristics of complex networks, identify key nodes and important modules, and gain insights into the overall function of the network [9][10]. This information is crucial for discovering potential drug targets and understanding how they interact with proteins [7] [11]. Additionally, topological analysis can provide a robustness and vulnerability assessment of the network, enabling predictions about its response to various perturbations, such as drug effects [8].…”