Current advances in ligand‐based target prediction
Cheminformatics
Identification
Repurposing
Similarity (geometry)
Drug target
Predictive modelling
Drug Development
DOI:
10.1002/wcms.1504
Publication Date:
2020-10-15T06:48:09Z
AUTHORS (8)
ABSTRACT
Abstract Target identification for bioactive molecules augments modern drug discovery efforts in a range of applications, from the elaboration mode‐of‐action drugs to repurposing even knowledge side‐effects and further optimization. However, traditional labor‐intensive time‐consuming experiment methods obstructed development. Driven by massive bioactivity data deposited chemogenomic databases, computational alternatives have been proposed widely developed expedite validation process. By screening compound against protein database, it is possible identify potential target candidates that fit with this specific subsequent experimental validation. In particular, ligand‐based prediction made tremendous progress past decade due their flexibility, relatively low cost, remarkable predictive performance, are still moving forward. review, we present comprehensive overview including similarity searching, machine learning algorithm stacking, strategies validate these methods. We also discuss strength weakness existing sources model development outline challenges prospects prediction. It expected topic discussed review should guide application be interest audiences wider scientific community. This article categorized under: Data Science > Chemoinformatics
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