Insilico Medicine's AI-designed drug candidate INS018_055

government

2021·PubMed Central(peer-reviewed)·pmc.ncbi.nlm.nih.gov/articles/PMC12298131/

Author

Rick Mullin

Credibility Rating

4/5

High(4)

High quality. Established institution or organization with editorial oversight and accountability.

Rating inherited from publication venue: PubMed Central

A systematic review examining AI applications in drug discovery and development (2015-2025), demonstrating how machine learning and molecular modeling accelerate pharmaceutical development timelines and outcomes.

Paper Details

Citations

Year

2021

Methodology

peer-reviewed

Metadata

journal articleanalysis

Summary

This systematic review examines how artificial intelligence is transforming drug discovery and development across various stages, from hit identification to lead optimization. The study analyzes research published between 2015-2025 and categorizes AI applications by methodology, clinical phase, and therapeutic area. The review demonstrates that AI—particularly machine learning (40.9%) and molecular modeling/simulation (20.7%)—significantly accelerates drug discovery timelines and improves clinical outcomes. The findings highlight AI's growing role in enhancing the speed and precision of identifying drug candidates and optimizing their efficacy across multiple therapeutic domains.

Cited by 1 page

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