In depth editorial article published by Innovations in Pharmaceutical Technology, introducing cluster synthesis as a major breakthrough enabling acceleration of small molecule discovery.
You will find the link to the full article below the excerpt:
While artificial intelligence now accelerates molecular design at unprecedented speed, laboratory synthesis continues to lag behind, limiting the impact of digital innovation. Emerging frameworks such as AI-driven orchestration and cluster synthesis offer a path to reshape how laboratories align design with execution.
The two sides of the modern efficiency gap
Generative models can now propose thousands of high-quality molecular structures in hours. Yet, many of these ideas sit in regions of chemical space that are difficult – or sometimes effectively impossible – to synthesise. Even with the growing use of synthetic-accessibility predictors for compound prioritisation, proposed compounds may still require lengthy multi-step routes, creating downstream bottlenecks that undermine the value of rapid design cycles. This disconnect has organisational consequences: while computational chemists readily embrace AI-enabled exploration, medicinal and synthetic chemistry teams often face feasibility barriers that weaken confidence in the outputs, leading to disparate technology adoption across departments.
On the execution side, automation has not kept pace with this surge in design diversity. For decades, chemistry was driven primarily by manual synthesis – highly skilled, but inherently sequential and limited in capacity. Automation was introduced to expand throughput, enabling high-throughput optimisation of a single transformation or systematic library synthesis around a shared scaffold; approaches that excel when workflows are repetitive and uniform. As synthesis demands grew, organisations increasingly turned to CRO networks – both specialised onshore groups and large offshore operations – to further expand throughput and absorb routine or labour-intensive chemistry.
Yet, neither outsourcing nor robotics addresses the core challenge. Robotic platforms remain constrained by rigid, transformation-specific workflows, and CRO reliance expands capacity but introduces variability, long cycle times and other outsourcing risks. As a result, the design-make gap widens; ideas accelerate, but synthesis remains anchored in processes optimised for uniformity, not diversity. This imbalance limits how quickly experimental data can be generated, fed back into models and used to guide the next round of design – reinforcing synthesis as the rate-limiting step in the DMTA cycle.
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