As demand for improved crop protection grows, new molecules need to meet ever more stringent criteria – not only in efficacy, but also sustainability and environmental protection.
An event organised by SCI and the RSC, Innovations in Crop Protection towards Sustainable Agriculture, today brought together industry leaders and innovators to share how advances in areas from molecule design to machine learning are helping to accelerate agrochemical research.
New molecules will need to adapt to regulatory changes – this article from the latest C&I describes the EC's recent response to calls for a phase-out of synthetics.
Development of new molecules is a time-intensive and expensive process. As Fides Benfatti, Head of Insect Control and Disease Control Research Chemistry at Syngenta Crop Protection explained, since the early part of the 20th Century, when nicotine was used in crop protection, new commercial compounds have been developed around every ten years.
‘Delivering safe and more environmentally sound compounds is essential for modern farming. Many old compounds no longer work. In addition, new crops present new challenges,’ Benfatti said.
To increase the rate of innovation from the lab to the customer, Syngenta has employed digital tools, where data-enabled insights can drive novel, augmented or targeted innovation.
Developing Plinazolin, a broad-spectrum insecticide and acaricide with a novel mode of action, Benfatti set out how Syngenta used several tools to get the final product to farmers.
Fellow Syngenta research leader, Dave Hughes, recently gave this free SCItalk on the future of farming.
‘There are several criteria that any molecule we bring to market need to meet. Using digital tools, we were able optimise our research,’ Benfatti commented.
To meet the company’s holistic product profile, Syngenta used shift-left testing, which Benfatti described as ‘a paradigm shift for us, allowing for faster design, synthesis, test, analysis cycles, leading to the best compound that met the criteria needed.’
‘While we had a molecule where potency was not an issue, we needed to meet the criteria for safety and selectivity among other things.’
In addition to shift-left testing Benfatti shared how Syngenta is also using multi-parameter optimisation (MPO) to identify high quality compounds with a balance of desirable properties.
‘MPO allowed us to rank compounds, giving us the ability to understand the differences between a number of molecules, so that we can then bring forward candidates with different risk profiles and decide which compound best fits our criteria,’ said Benfatti. ‘MPO also allows us to build a library of compounds that we can revisit with a view to potentially optimising at a later date.’
In this article from the latest edition of C&I, Lucy Wright looks at how researchers in Saskatchewan, Canada, are developing AI deep learning models to more easily interpret aerial photography to determine crop health and management strategies required.
Syngenta is also employing direct design and inverse design as part of its tool kit for molecule and product development. ‘In direct design, compounds are selected for synthesis if they meet a set criterion or serve to validate a theory. Inverse design generates only compounds meeting set criteria,’ Benfatti said.
On a day when BT announced it will cut 55,000 jobs and replace up to 20% of those with AI by the end of the decade, Benfatti noted that the possibilities offered by digital tools are set to have a big impact on the development of new molecules and agricultural products, but that chemists and scientists will remain at heart of new developments.
‘Digital tools will not replace chemists, but chemists supported by digital tools will outperform chemists without them.’