Feature article - Can science-smart AI accelerate chemical R&D?

Elizabeth Altizer, CAS SciFinder* product manager, explains how AI built for scientists is transforming chemical R&D

Chemical R&D is not for the impatient. In pharmaceuticals, the journey from concept to market can span 10 to 15 years and cost more than $2.6 billion1. Across speciality chemicals, researchers face mounting pressure to reduce time-to-market, respond to shifting consumer demands and navigate complex data landscapes shaped by interdisciplinary convergence, regulatory scrutiny, and sustainability goals.

In the early 2010s, digitalisation emerged as a transformative force in scientific research. It aimed to replace fragmented, analogue workflows with integrated digital systems, making lab notebooks shareable, data more accessible, and institutional knowledge easier to preserve. 

For many organisations, this improved collaboration and accelerated documentation. However, it did not resolve the deeper epistemological challenge: how to extract meaningful insights from an ever-expanding body of scientific knowledge.

Today, researchers face information overload. Literature, patents, internal reports and market signals accumulate faster than any individual or team can reasonably process. Artificial intelligence (AI) is considered the next step in addressing this bottleneck. It offers the potential to surface connections, identify patterns and support decision-making at scale. 

Why AI has not delivered—yet

Despite its promise, AI remains underutilised in chemical R&D.1 Three out of four researchers report using AI tools once a month or less. Only 10% believe these tools reliably answer scientific questions.2 In disciplines where precision is paramount, trust in AI remains fragile.

This hesitation is not rooted in resistance to innovation, but in a principled demand for transparency. Scientists are trained to value reproducibility and to trace conclusions back to their sources. When AI systems produce results without revealing how they arrived there, they introduce a ‘black box’ dynamic, where the lack of transparency undermines scientific reproducibility. If identical inputs yield different outputs, or if the provenance of a result is unclear, confidence erodes.

The issue is twofold: trust and relevance. Most AI tools were not designed for scientific contexts. They are general-purpose systems attempting to solve specialised problems. That is where science-smart AI used by CAS SciFinder offers a meaningful alternative, one built on curated scientific data and developed in partnership with researchers themselves.

What makes AI ‘science-smart’?

CAS approached AI differently. Our models are trained on the CAS Content Collection*, the world’s largest human-curated database of chemistry and related sciences. That means the AI learns from peer-reviewed literature, patents and expertly indexed data, not from general web content.

We also built it in collaboration with scientists. Our teams of PhD chemists, data scientists, and researchers worked together to ensure the system understands the language and logic of scientific inquiry. 

As always, we prioritised transparency. For example, when CAS SciFinder uses AI to optimise a search query, users can view the interpretation and choose to revert it to their original input. This is not AI that replaces scientists, but it supports their workflows. 

Science-smart AI is now embedded into CAS SciFinder. The latest release introduces three capabilities that apply AI where it matters most, helping researchers search more intuitively, plan more efficiently, and assess intellectual property more confidently.

• SearchSense: A natural language search tool that understands scientific questions as they’re asked. Instead of crafting complex queries, researchers can type “boiling point of water” and receive a direct answer, along with links to the underlying data. In beta testing, 93% of users reported increased efficiency
• Interactive Retrosynthesis: The first real-time, interactive synthetic planning tool of its kind. Researchers can generate and modify synthesis plans in seconds, adjusting steps based on available reagents, equipment, or other constraints. It is a significant advancement in speed and flexibility
• IP Connections: An AI-enhanced set of features and data visualisations that help researchers identify relevant patent and non-patent literature from free-text input. It is especially useful for early-stage prior art searches, supporting better decisions before entering the lab or filing a claim

Real-world impact

These capabilities are already reshaping how researchers approach complex problems. One scientist used SearchSense in CAS SciFinder to explore a new line of inquiry involving a known compound and a broad class of reactants. What previously required hours of iterative structure-based searching was completed in minutes using natural language input. The result was faster access to relevant reactions and a more agile research process.

In another case, a researcher drafting a patent application leveraged the AI-enhanced IP Connections feature to uncover prior art that traditional keyword strategies had missed. By inputting early-stage claim language, they identified overlapping literature before submission. This helped avoid costly revisions and reinforced the value of integrating IP awareness earlier in the innovation cycle.

Why this matters now

The volume of scientific data grows every year. Researchers are expected to analyse literature, assess IP landscapes, consider sustainability and still deliver results. As such, the opportunity cost of pursuing the wrong path is high. If a team spends months on a synthesis route only to discover it is already patented, that is time and budget they cannot recover.

Science-smart AI helps mitigate that risk. It surfaces relevant information faster, supports better decision-making, and frees up researchers to focus on what they do best: innovating.

The future of AI in chemical R&D lies in connecting the dots, identifying relationships between disparate data points, suggesting hypotheses, and helping scientists explore new directions. CAS is exploring how AI can traverse publications, patents and experimental data to uncover insights that are not explicitly stated but are scientifically sound.

AI will not replace the scientist. Science-smart AI, built on trusted data and designed for real-world workflows, can be a powerful partner. CAS is also committed to ethical and responsible AI development.

* - SciFinder and The CAS Content Collection are registered tradesmark of CAS

Contact 

Joe Singh

Senior Communications Manager

CAS

[email protected]

References:

1. B. Chatterjee et al. Pharm. Res. Jan 2024, 41(1):7-1

2. Marketing for the Chemical Sciences Survey Report 2025, C&EN BrandLab, ACS