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Strategic Key Performance Indicators for AI in Lead Optimization.

Theodor Theis1, Stefanie Flohr2, Hayley Binch3

  • 1Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, Biberach an der Riß, Germany.

Chemmedchem
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers promise for pharmaceutical R&D productivity. New impact-oriented key performance indicators (KPIs) are needed to measure AI

Keywords:
artificial intelligencecomputational chemistrykey performance indicators

Related Experiment Videos

Area of Science:

  • Drug Discovery
  • Computational Chemistry
  • Medicinal Chemistry

Background:

  • Pharmaceutical R&D faces persistent challenges with high costs and failure rates.
  • Artificial intelligence (AI) integration in R&D shows potential to enhance productivity, with AI-native companies potentially outperforming traditional ones.
  • Current R&D metrics focus on technical model performance, not strategic impact, hindering AI's full potential.

Purpose of the Study:

  • To address the need for strategic impact measurement in AI-driven drug discovery.
  • To discuss challenges in applying Key Performance Indicators (KPIs) within the unique drug discovery environment.
  • To propose a new framework of impact-oriented KPIs for AI in R&D.

Main Methods:

  • Perspective piece based on discussions within the EFMC2 community.
  • Analysis of the shift from expert-driven computer-aided drug design (CADD) to semi-autonomous AI.
  • Development of recommendations for designing strategic KPIs.

Main Results:

  • Measuring technical model performance is insufficient for assessing AI's strategic impact in drug discovery.
  • The transition to AI necessitates a shift towards impact-oriented KPIs.
  • Effective KPIs should drive adoption, foster innovation, and objectively assess digital tool performance.

Conclusions:

  • A new framework of impact-oriented KPIs is crucial for realizing AI's potential in pharmaceutical R&D.
  • Strategic KPIs will help objectively evaluate the effectiveness of digital tools in drug discovery.
  • Implementing these KPIs will guide the adoption and innovation of AI in the R&D landscape.