Accelerating Early Drug Discovery Through Automated Medicinal Chemistry

Medicinal Chemistry & Chemical Biology Highlights

Authors

  • Sophie Racine Novartis, Global Discovery Chemistry, Fabrikstrasse 16, CH-4056 Basel
  • Peter Meier Novartis, Global Discovery Chemistry, Fabrikstrasse 16, CH-4056 Basel
  • Cara E. Brocklehurst Novartis, Global Discovery Chemistry, Fabrikstrasse 16, CH-4056 Basel
  • Frédéric Berst Novartis, Global Discovery Chemistry, Fabrikstrasse 16, CH-4056 Basel

DOI:

https://doi.org/10.2533/chimia.2026.721

Keywords:

Automated medicinal chemistry, Data foundation, DMTA cycle, Magnetic solid phase synthesis, Microscale chemistry

Abstract

The discovery of new medicines relies on the iterative Design, Make, Test and Analyse (DMTA) cycle. Increasing the speed of this cycle is key to accelerating drug discovery and delivering new medicines to patients faster. As a result, pharmaceutical companies are closely monitoring and optimising their DMTA processes to increase efficiency. Over the last two decades, advances in automation have progressively transformed medicinal chemistry, enabling more efficient synthesis, purification, and profiling of new compounds. At Novartis Global Discovery Chemistry, these efforts have led to innovative capabilities such as MicroCycle, which combines microscale synthesis, automated purification, miniaturised profiling, and predictive modelling into a single platform. By generating highly information-rich datasets from minimal material consumption, MicroCycle accelerates decision-making while supporting sustainable drug discovery practices. A major recent advance has been the adaptation of magnetic bead technology from biology to organic synthesis, addressing one of the longstanding challenges of automated medicinal chemistry: the efficient execution of multistep synthetic sequences in miniaturised, platebased formats. By enabling the automated synthesis of complex molecules and expanding the accessible chemical space to high-throughput workflows, magnetic beads represent a transformative advance in the way chemical matter is explored and optimised. Together, these innovations illustrate how automation, data generation, and machine learning are reshaping medicinal chemistry and paving the way towards increasingly autonomous, AI-enabled discovery laboratories.

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Published

2026-10-28

How to Cite

[1]
S. Racine, P. Meier, C. E. Brocklehurst, F. Berst, Chimia 2026, 80, 721, DOI: 10.2533/chimia.2026.721.