arXiv:2606.01846v1 Announce Type: new Abstract: 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 gene
The increasing resistance of mosquitoes to conventional insecticides necessitates innovative solutions, and advancements in generative AI are now enabling novel drug discovery approaches.
This development represents a critical step in combating mosquito-borne diseases globally and demonstrates the power of AI in accelerating synthetic biology and drug design.
The paradigm shifts from activity prediction of known molecules to the de novo generation of entirely new molecular scaffolds for insecticide development, offering a pathway to overcome resistance.
- · Public Health Organizations
- · Pharmaceutical Companies
- · AI-driven Drug Discovery Platforms
- · Populations in Endemic Regions
- · Conventional Chemical Insecticide Manufacturers (long-term)
- · Mosquitoes
New, more effective insecticides will be developed, reducing the incidence of mosquito-borne diseases.
This success could accelerate the application of generative AI in other areas of drug discovery and materials science.
The reduced health burden could lead to economic growth and increased stability in affected regions, potentially shifting geopolitical influence.
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Read at arXiv cs.LG