A Comprehensive Review of Cheminformatics Approaches for Compound Activity Analysis and Future Prospects

SCIENCE

Cheminformatics has emerged as a powerful and indispensable discipline in the field of drug discovery, revolutionizing the way compounds are analyzed for their activity against specific biological targets. This review paper provides a comprehensive overview of the various cheminformatics approaches utilized for compound activity analysis. We explore the applications of molecular descriptors, machine learning techniques, and virtual screening methods in predicting compound activity. Additionally, the integration of omics data and the challenges faced in cheminformatics are discussed. The review also presents case studies showcasing successful applications of cheminformatics in real-world drug discovery projects. The findings highlight the significance of cheminformatics in accelerating drug discovery processes and offer valuable insights into its potential future prospects.

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