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. 
Close-up of a colorful molecular structure with red, orange, and purple atoms linked by clear rods against a blurred blue scientific background
Yield
Optimized reactions
Cost
R&D savings
Speed
Faster development
Researcher in a white lab coat stands in a blue-lit high-tech lab, facing shelves filled with glass bottles and vials, with digital touchscreens on both sides.
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
Busy chemistry lab with blurred researchers using pipettes, centrifuges, glassware, and automated equipment around benches of orange liquid samples.
High number of laboratory experiments required to optimize yield
Masked lab worker in hairnet and safety glasses uses a pipette to drop liquid into a test tube with blue solution, wearing blue gloves.
Complex decision-making around reaction routes and conditions
Automated stainless-steel filling line pours amber liquid into glass jars, with cleanroom workers blurred in a blue-lit manufacturing lab.
Pressure to shorten time-to-market without sacrificing quality
Solution
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Developed a GenAI-based synthesis optimization tool 

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Implemented retrosynthesis models trained on patent reactions 

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Applied reaction similarity analysis to suggest initial conditions 

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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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