Defeating pests with AI models: Our first-principles approach

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In this short blogpost, we’ll be expanding upon some of our core technology, and our approach to molecular discovery for pesticides. In general, we view pesticide discovery as a search space problem, where we’re trying to optimize for multiple different (sometimes conflicting) properties of small-molecules and peptides. We believe that foundational models have become powerful tools for predicting these different properties, and hence can be leveraged with generative pipelines to effectively find pesticides. We support this hypothesis by showing in-vitro data for pesticides discovered by our model, targeting Spodoptera Frugiperda, an invasive pest species that causes $10 Billion worth of damages every year. Pesticides are substances used to control and kill pests. The word ‘pests’ is a very broad one, and as such, pesticides is a ‘catch-all’ term for substances that are used to kill insects, control invasive weeds (herbicides). Pesticides are essential for 98% of all farmlands. Good! No

Defeating pests with AI models: Our first-principles approach EN EN English 中中文 हि हिंदी In this short blog post, we’ll be expanding upon some of our core technology and our approach to molecular discovery for pesticides. In general, we view pesticide discovery as a search space problem, where we’re trying to optimize for multiple different (sometimes conflicting) properties of small molecules and peptides. We believe that foundational models have become powerful tools for predicting these different properties, and hence can be leveraged with generative pipelines to effectively find pesticide

Defeating pests with AI models: Our first-principles approach

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