An AI-Driven Chemogenomics Knowledgebase for Human-Transmissible Pathogens: A Platform for Antimicrobial Drug Discovery

Abstract Emerging infectious diseases (EIDs) pose a critical threat to global biosecurity. Integrating bio/chemical information and artificial intelligence would bring new strategies for antimicrobial drug discovery. Herein, the Human Pathogenic Microorganisms Chemogenomics Knowledgebase (HPM-CKB) is presented as the largest domain-specific resource, consolidating chemical, genetic, and proteomic data on human-transmissible pathogens, together with multiple computational functional modules. The current release covers 7876 pathogenic proteins from 267 microorganisms (including 13,914 protein 3D structures) and 234,287 associated with bioactive molecules. HPM-CKB enables large-scale virtual screening, target identification, and drug repurposing, and integrates a large language model (LLM) for interactive queries. The computational prediction performance of HPM-CKB is corroborated by known inhibitors targeting SARS-CoV-2 replicase polyprotein 1ab. In wet-lab validations, four approved drugs (cefixime, ceftazidime, saquinavir, and rilapladib) identified via virtual screening show binding activity to SARS-CoV-2 nucleoprotein in affinity assays and inhibit SARS-CoV-2 replication in Vero E6 cells, demonstrating HPM-CKB’s potential in drug repurposing. Meanwhile, two anti-Staphylococcus aureus lead compounds with novel scaffolds (CYC-HXL-9124 and CYC-HXL-9126) are identified via deep learning, and the potential target protein, cell division protein FtsZ, is subsequently prioritized using HPM-CKB (http://cgai.asia/g/pathogenDB) and experimentally validated by affinity assays. Collectively, these findings establish HPM-CKB as both a chemogenomic knowledgebase and a systematic drug development platform against EIDs.