From Decades to Months: AI-Powered Molecular Discovery

As generative AI transforms chemical R&D from a decade-long, billion-dollar investment into a data-driven, autonomous engine, AUTOMA Chem 2026 (26–27 October, Berlin) brings together industry leaders to examine how AI-powered molecular discovery is delivering measurable ROI.

In the world of chemical and pharmaceutical R&D, time is the most expensive resource. With industry data showing that traditional molecular discovery can take over a decade and cost upwards of $1 billion per asset, the integration of AI  is now a key competitive advantage.

Recent studies suggest that AI-driven R&D can minimise discovery timelines by up to 50%, translating to hundreds of millions in saved operational costs and accelerated time-to-market.

The primary profit driver of AI in discovery is the significant reduction of physical trial-and-error. By predicting molecular properties and synthetic pathways in silico, companies can eliminate dead-end experiments before a single reagent is used. This transition from empirical guessing to predictive precision is fundamentally changing the ROI of R&D departments.

To capture these gains, the physical lab needs to change. Jose Seoane, Technology Advisor at Repsol Materials S.A., covers this in the presentation at AUTOMA Chem 2026. As the industry moves toward 24/7 autonomous experimentation, he reveals how self-driving labs, combining AI design with robotic execution, are allowing Repsol to test hypotheses at machine speed, drastically lowering the cost per experiment and accelerating the commercialisation of new materials.

However, an AI-designed molecule is only profitable if it can actually be synthesised. Tackling the synthesis obstacle, Dr. Ewa Gajewska, Head of Product Management for SYNTHIA® at Merck, demonstrates during at AUTOMA Chem 2026 how the company’s digital retrosynthesis tools ensure that the molecules generated by AI are not just theoretically innovative but practically and economically manufacturable. 

AUTOMA Chem 2026 closes the gap between AI discovery and commercial synthesis with real case studies from Evonik, Covestro, Worley, Veeva Systems, Siemens AG, Nokia and others. Stay ahead of the competition in chemical innovation: https://sh.bgs.group/4lf