The buzz around AI in drug discovery often centers on generating new molecular ideas. But as computational chemists know, an in silico design is only valuable if it can be efficiently synthesized in the lab and is manufacturable after that. Merck KGaA, Darmstadt, Germany, with its roots in both chemistry (via Sigma-Aldrich) and life science technology, is tackling this “make” step. Its SYNTHIA Retrosynthesis Software uses AI to map out viable synthetic pathways, directly connecting computational design to the practicalities of the bench and potential scale-up, even linking to commercially available starting materials. Ewa Gajewska, Head of Product Management for SYNTHIA, who is speaking at PHARMAP 2025 (April 14-15, Berlin), explains how the software streamlines this critical transition.
In the following interview, Gajewska touchs on how AI-driven retrosynthesis is transforming drug development from lab to production. Responses have been lightly edited for brevity.
As Head of Product Management for SYNTHIA at Merck KGaA, Darmstadt, Germany, could you explain how SYNTHIA Retrosynthesis Software is changing the way pharmaceutical companies approach drug development and manufacturing?
Ewa Gajewska
Gajewska: SYNTHIA Retrosynthesis Software changes drug development by automating retrosynthetic analysis. Traditionally, chemists spent a lot of time manually mapping out potential synthetic routes. With advanced algorithms and AI, SYNTHIA handles this process quickly, generating and comparing many possible pathways, which saves time and money.
SYNTHIA also uncovers creative synthetic options. It uses a large database of expert-coded reaction rules and computational models to spot routes that might not be obvious to human chemists—especially for novel compounds. This approach can lead to faster, more scalable manufacturing, and SYNTHIA’s forward synthesis feature can even predict entirely new drug structures.
SYNTHIA aligns with the growing push for sustainable pharmaceutical manufacturing by flagging routes that use fewer steps or less hazardous reagents. This helps meet regulatory standards and supports environmental goals. At Merck KGaA, Darmstadt, Germany, we find that SYNTHIA can accelerate both innovation and efficiency.
By blending human expertise and AI, SYNTHIA helps companies innovate faster, cut costs, and adopt greener practices. From early research to industrial-scale production, it’s a powerful tool for discovering new therapies.
The 2024 webinar “AI meets chemistry” showcased how SYNTHIA integrates with AI systems for drug discovery. Can you share your perspective on how this technology helps bridge the gap between drug discovery and commercial-scale manufacturing?
Gajewska: As highlighted in the webinar, SYNTHIA automates the search for ideal synthetic pathways, speeding up planning and reducing human error. It ensures these routes are practical and scalable for industrial use.
A key feature is SYNTHIA’s Synthetic Accessibility Score (SAS) API, which rates a molecule’s complexity so researchers can focus on targets that are easier to make. This helps prioritize promising candidates, saving time and resources. This focus on feasibility helps SYNTHIA meet the industry’s demand for efficiency across discovery and manufacturing.
What is the current maturity level you see with AI in chemistry in pharma contexts in 2025?
Gajewska: AI in chemistry is advanced but still growing. Platforms like SYNTHIA significantly improve retrosynthesis and molecule selection, yet full integration into every step of drug development is ongoing. Reaction optimization still needs more reliability, and labs must adapt to incorporate AI tools smoothly.
AI in chemistry isn’t just a novelty—it’s becoming a standard part of R&D. Companies use AI-driven systems to accelerate discovery, refine synthetic routes, and control costs. By 2025, we expect AI to drive breakthroughs at a scale once thought impossible.
What specific manufacturing or production challenges can SYNTHIA help solve that traditional approaches cannot address as effectively?
Gajewska: SYNTHIA tackles several issues with scaling up complex molecules. Manual retrosynthesis can be slow and prone to mistakes, but SYNTHIA automates it, cutting planning time and offering multiple viable routes. It also evaluates cost and scalability from the start, minimizing headaches later on. The Synthetic Accessibility Score (SAS) API guides teams to molecules that are simpler to produce, reducing wasted time. For time-sensitive projects such as new antiviral drugs, SYNTHIA speeds up the path from discovery to production. Plus, it considers reagent availability and cost, helping teams plan around real-world manufacturing constraints.
How are pharmaceutical companies using SYNTHIA to address sustainability concerns in their manufacturing processes? Can AI-driven synthesis planning help reduce waste and resource consumption?
Gajewska: Many companies, including GSK, use SYNTHIA to design more sustainable synthetic pathways that produce less waste and require fewer or safer reagents. Because SYNTHIA maps out greener routes, it cuts trial-and-error efforts and helps researchers choose sustainable materials. This saves energy, lowers environmental impact, and keeps production efficient.
our software helps plan chemical syntheses, but implementation often requires adaptation for manufacturing scale. Can you say more on how SYNTHIA helps scientists and engineers make this transition from lab to production scale?
Gajewska: SYNTHIA goes beyond proof-of-concept. It checks for cost-efficiency, reproducibility, and the availability of reagents in bulk. By comparing multiple routes, SYNTHIA highlights the ones best suited for large-scale production. It factors in reaction conditions and streamlines the process to reduce bottlenecks as you scale up. SYNTHIA also promotes collaboration by letting teams share synthetic plans, connecting research and manufacturing more smoothly.
In your recent publications, you’ve explored computer-generated “synthetic contingency” plans for supply chain challenges. How can pharmaceutical manufacturers tap this capability to enhance resilience in their production processes?
Gajewska: SYNTHIA can build “synthetic contingency” plans that prepare companies for supply chain surprises. It spots alternative routes relying on more common, cheaper reagents, so you’re less vulnerable if a key material is in short supply. This flexibility keeps production stable and helps you adapt quickly to sudden changes in availability or costs.
What role do you see AI and computational chemistry tools like SYNTHIA playing in the future of pharmaceutical packaging innovation?
Gajewska: Eventually, we’ll see AI-enabled packaging, like embedded sensors that track drug stability or indicate expiration. By combining computational chemistry with materials science, drugmakers can create packaging that’s both sustainable and responsive to real-world conditions.
How is SYNTHIA evolving to meet the specific needs of the pharmaceutical manufacturing sector, especially regarding regulatory compliance and quality control?
Gajewska: SYNTHIA pinpoints synthetic routes that favor safety, reproducibility, and compliance with regulations. It can flag potential side reactions or impurities, helping refine processes to ensure product quality. Its emphasis on scalability and cost-effectiveness supports stable and efficient production. As digital integration progresses, SYNTHIA will continue to smooth out workflows, making it a key tool in pharmaceutical manufacturing.
Hear more from Ewa Gajewska on the role of AI and tools like SYNTHIA in pharmaceutical development and manufacturing at PHARMAP 2025 (April 14-15, Berlin).
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