High-Throughput Strategies in Chemical Synthesis

High-throughput experimentation (HTE) has transformed chemical synthesis by enabling rapid, parallel evaluation of reaction conditions on scales ranging from nanolitres to millilitres. Automated platforms combine liquid‐handling robotics, wellplate arrays and miniaturised reactor systems to screen diverse reagent combinations, catalysts and parameters in a single run. Coupled with advanced data‐management software and statistical analysis tools, these approaches accelerate reaction discovery, optimisation and mechanistic insight. Integration of machine learning further enhances predictive capability, guiding the selection of promising substrates and conditions while reducing experimental burden. High-throughput strategies now underpin late-stage functionalisation in drug development, large‐scale catalyst screening and explorations of reaction generality across chemical space. By improving resource efficiency and offering real-time feedback, HTE fosters greener, more sustainable workflows and democratises access to complex synthetic methodologies in both academic and industrial settings.

Research from Nature Portfolio

Recent studies have introduced software that seamlessly links experimental design, execution and analysis in high-density wellplate formats. Users can define reaction arrays virtually, access reagent inventories, and generate instructions for manual or robotic operation, with all data stored in machine-readable formats to guide iterative experimentation. Complementing this, platforms combining geometric deep learning with HTE have enabled predictive modelling of reaction yields, regioselectivity and substrate compatibility in late-stage functionalisation of drug candidates, accurately identifying diversification opportunities across complex molecules. A further advance is a statistically rigorous framework for analysing large HTE datasets, unveiling hidden correlations between reagents, conditions and outcomes. The release of tens of thousands of proprietary reaction records underlines the power of this approach to map broad chemical spaces, quantify dataset bias and pinpoint under-explored reaction domains for future study.

High-Throughput Strategies in Chemical Synthesis publication trend

The graph below shows the total number of articles in high-throughput strategies in chemical synthesis across all publications each year (not limited to Nature Index journals).

Technical terms

High-Throughput Experimentation (HTE): Parallel execution and analysis of multiple reactions under varying conditions to accelerate discovery and optimisation.

Late-stage functionalisation: Modification of complex molecules, often drug candidates, at a final synthetic stage to tune properties without de novo synthesis.

Geometric deep learning: Machine-learning techniques that incorporate molecular graph structures and spatial information to predict reaction outcomes.

Acoustic dispensing: A contactless liquid-handling method that uses sound waves to transfer nanolitre droplets into wellplates for miniaturised synthesis.

Reaction wellplate: A microtitration plate (commonly 24, 96, 384 or 1536 wells) used to conduct parallel chemical experiments in small volumes.

References

1. Rapid planning and analysis of high-throughput experiment arrays for reaction discovery. Nature Communications (2023).
2. Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning. Nature Chemistry (2023).
3. Probing the chemical ‘reactome’ with high-throughput experimentation data. Nature Chemistry (2024).
4. Machine-Learning-Guided Discovery of Electrochemical Reactions. Journal of the American Chemical Society (2022).
5. Nanoscale, automated, high throughput synthesis and screening for the accelerated discovery of protein modifiers. RSC Medicinal Chemistry (2021).
6. ‘Chemistry at the speed of sound’: automated 1536-well nanoscale synthesis of 16 scaffolds in parallel. Green Chemistry (2023).
7. High‐Throughput Experimentation as an Accessible Technology for Academic Organic Chemists in Europe and Beyond**. Chemistry - Methods (2023).
8. Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality. ACS Central Science (2024).
9. Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery. Nature Communications (2023).