Accelerating molecule synthesis with AI optimization

A pharmaceutical R&D team reduced experimental cycles and costs by applying AI-driven retrosynthesis and reaction optimization to molecule synthesis.

Yield

Optimized reactions

Cost

R&D savings

Speed

Faster development

From trial-and-error synthesis to AI-guided decisions

Researchers needed to select optimal synthesis routes, define reaction conditions, and maximize yield while controlling costs and impurities.

Objectives

Reduce experimental iterations, accelerate development timelines, and lower R&D costs.

Opportunity

Molecule synthesis is a costly and time-intensive process requiring rapid optimization to remain competitive in drug development.

Driving Complex Global Transformations

Proven delivery overcoming complex multi-country business challenges

Key Challenges

High number of laboratory experiments required to optimize yield

Complex decision-making around reaction routes and conditions

Pressure to shorten time-to-market without sacrificing quality

Solution

Developed a GenAI-based synthesis optimization tool

Implemented retrosynthesis models trained on patent reactions

Applied reaction similarity analysis to suggest initial conditions

Used Bayesian optimization to recommend optimal reaction parameters

Impact

Faster development, lower cost

Reduced experiments required to reach optimal yield by 64 percent.

Lowered R&D costs by an estimated 3 to 9 percent.

Accelerated drug development timelines.

Improved decision quality in early-stage synthesis.

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