AI in Computer Aided Synthesis Planning Market Trends

Growth Drivers

• Rising adoption of AI-driven green chemistry for sustainable routes: The regulatory and societal pressure to adopt environmentally sustainable chemical processes has increased in the current decade. This change is a key factor driving the expansion of AI in the CASP market. The use cases of AI algorithms have improved synthesis pathways by predicting the reaction outcomes. Furthermore, the advent of regulatory push, such as the EU’s Green Deal, ties up with the rising demand for AI-powered synthesis planning to reduce the ecological footprint of chemical processes. Additionally, with the rising funding for green chemistry innovations, the demand is predicted to expand by the end of 2035.
• Growing integration of generative AI models for novel molecule discovery: The rapid rise of generative AI models has significantly improved CASP use cases. Gen AI models assist in the autonomous design of novel chemical structures with tailored properties. In terms of measurable impact, the drug discovery timelines have been reduced in specific cases due to the application of AI. For example, the ACS Publications study in 2023 has stated that Insilico Medicine utilized gen AI platform Chemistry42 to identify a novel antibiotic candidate and was successful to combat methicillin-resistant Staphylococcus aureus.
• Government funding for AI in healthcare: Government investment for AI in synthesis planning is a key driver demanding the market. As per the World Economic Forum report in November 2024, the investments of Venture-capital in health AI in the U.S. reached USD 11 billion. These funding create a AI-ready datasets for therapeutic development, including synthesis planning. This non-dilutive funding de-risks early-stage innovation and stimulates market growth by supporting foundational research that private companies can commercialize.

AI-Discovered Small Molecules in Clinical Development

Source: NLM August 2025

Challenges

• Complexity in balancing scalability with security in AI-driven synthesis platforms: The surging adoption of AI in CASP has led to the demand for scalable platforms to handle chemical datasets. The surging demand has created an impediment in scaling platforms as they often outpace the cybersecurity training of employees, expanding the vulnerability to complex cyberattacks. Additionally, another challenge to scaling is the requirement to adhere to strict data protection regulations, as it can delay deployment.
• Development cost and uncertain reimbursement: The R&D for durable AI synthesis platforms is expensive, involving multidisciplinary teams. A strong impediment is the lack of well-defined reimbursement codes by payers such as the U.S. Centers for Medicare & Medicaid Services (CMS). In the absence of a well-defined mechanism for the reimbursement of healthcare systems using these products, their uptake is inhibited, thus presenting a large financial hurdle for manufacturers who cannot prove a clear revenue path.