This invention provides an
artificial intelligence-based method for screening
antimicrobial drug molecules and its applications, specifically relating to the field of
drug screening technology. The method constructs a hierarchical
virtual screening system. First, it performs two-stage screening using an affinity prediction model and molecular docking tools, and then constructs the affinity prediction model through
machine learning. Subsequently, it integrates
drug-likeness assessments such as water
solubility and
toxicity, as well as molecular similarity calculations, to reduce the probability of obtaining low-drug-likeness drug molecules or potentially cross-resistant molecules, filtering candidate molecules hierarchically. Finally, it verifies
antimicrobial activity. This invention solves the problems of low efficiency and
system scarcity in screening hundreds of millions of molecules by integrating
deep learning with traditional computational methods. While improving screening accuracy, it introduces
drug resistance risk screening, effectively reducing research components and shortening the cycle, providing high-quality candidate molecules for the clinical translation of anti-drug-resistant drugs, and has significant clinical translational value and application prospects.