用AI生成新型杀蚊化合物，命中率78%。
Mos-Gen: A Generative Molecular Framework for Mosquito Insecticide Design
- 结合预训练模型与变分自编码器，生成含二硫键的蒜素衍生物。
- 14个候选物中9个预测阳性有78%实测有效，阴性全无效。
- 适合药物研发、绿色杀虫剂设计人员快速筛选分子。
蚊媒传染病每年导致全球逾70万例死亡。长期使用传统化学杀虫剂引发严重抗药性，亟需开发新型高效且生态可持续的替代方案。现有AI方法多聚焦活性预测与分类，却缺乏从头生成新颖分子骨架的能力。本文提出Mos-Gen，一种基于动机感知的生成协作框架，融合预训练分子表征模型Uni-Mol与变分自编码器（VAE），专为设计含二硫键的蒜素衍生物类蚊虫杀灭剂而优化。从生成候选物中选取14种——包括9个预测阳性与5个预测阴性——进行化学合成与实验验证。预测阳性的命中率达78%，而所有预测阴性均未表现出杀蚊活性。实验结果充分验证了Mos-Gen框架的高精度筛选能力。
原文摘要 · Abstract (English)
Mosquito-borne infectious diseases cause more than 700000 deaths worldwide each year. The long-term use of conventional chemical insecticides has induced serious resistance problems, creating an urgent need to develop novel, highly effective, and ecologically sustainable alternatives. While existing artificial intelligence approaches in this domain have focused primarily on activity prediction and classification, they leave a critical gap in the de~novo generation of novel molecular scaffolds. In this study, we propose Mos-Gen, a motif-aware generative collaborative framework that couples the pretrained molecular representation model Uni-Mol with a variational autoencoder (VAE), specifically tailored for the design of disulfide-containing allicin derivatives as mosquito insecticides. Among the generated candidates, fourteen compounds -- comprising nine predicted positives and five predicted negatives -- were selected for chemical synthesis and experimental validation. The hit rate among the predicted positives reached 78%, whereas none of the predicted negatives exhibited mosquitocidal activity. These experimental results fully validated the high-precision screening capability of the Mos-Gen framework.
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