Artificial intelligence (AI) is already changing the way medicines are discovered. Machine learning and other technologies are making the hunt for new pharmaceuticals quicker, cheaper and more effective. Our goal is to do the same for agriculture – to discover new, more effective crop protection solutions that safeguard the world’s food against diseases, weeds and pests, while also protecting ecosystems.
That is why we are putting these technologies at the heart of our research and development. Syngenta Crop Protection is collaborating with Insilico Medicine, a world leader in using AI and deep learning to produce very precise chemistry in the pharma and biotechnology sectors. Combining our skills, knowledge and technologies allows us to discover new molecules – which are the building blocks of our products – and even to design molecules that are more sustainable.
Syngenta scientists in a laboratory
By bringing the next generation of solutions to farmers faster and more efficiently, we are helping them to increase productivity and meet global demand for affordable, nutritious, high-quality food.
Protecting pollinators, beneficial insects and soil
Accelerating innovation has never been more important, as farmers face increasing challenges, from extreme weather events, to biodiversity loss, and the need to make soil healthier.
With AI, we can better use large data sets to create crop protection products that nurture our environment. We can be confident that new products protect biodiversity – including pollinators such as bees, which are vital to grow crops, and beneficial insects such as ladybugs, which are a natural way to control pests. We will also be able to design products that help to keep soil healthy, giving it the ability to capture carbon and so reduce agriculture’s greenhouse gas emissions.
Rice fields in India
Partnering with a pioneer
Insilico Medicine is a leader in the research of generative models, reinforcement learning (RL), and other modern machine learning techniques to develop platforms that rapidly design new molecular structures and generate synthetic biological data.