PDF(Pubmed)
Abstract：
The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products.
摘要：
在过去30年中，农药使用的全球成本效益分析的特点是1990年至2007年期间显着增加，随后下降。这一观察可以归因于几个因素，包括，但不限于,害虫抗性,在行动模式或化学类别方面缺乏新颖性，和监管行动。由于当前和预计的全球人口增长，很明显，对食物的需求，因此，使用杀虫剂来提高产量将会增加。应对这些挑战和需求，同时通过日益严格的监管环境推广新的作物保护剂，需要开发和整合创新的基础设施，新的和可持续的分子的成本和时间有效的发现和发展。在过去的二十年中，人工智能（AI）和化学信息学的重大进展提高了研究科学家在发现生物活性分子方面的决策能力。AI和化学信息学驱动的分子发现提供了将实验从温室转移到虚拟环境的机会，在虚拟环境中可以快速研究数千到数十亿的分子，为铅的产生提供无偏见的假设，优化，以及化合物合成和测试的有效建议。迄今为止,与药物发现相比，公开可用的农业化学研究文献在很大程度上说明了这一点。在这次审查中,我们提供了作物保护发现管道的概述，以及传统的，化学信息学，和人工智能技术可以帮助解决农业化学发现的需求和挑战，以快速开发新型和更可持续的产品。
