Polychlorinated biphenyls (PCBs) persist globally as legacy pollutants with a complex structural diversity that complicates the understanding of their microbial conversion processes and remediation. In this study, high-throughput enzymatic assays, quantum chemical calculations, and machine learning were integrated to elucidate the reductive dechlorination pathways and reactivity of all 209 PCB congeners. By coupling Hirshfeld charge analysis with empirically derived steric effects, 98.3% accurac
Decoding Microbial Reductive Dechlorination of 209 Polychlorinated Biphenyl Congeners through Experiment-Aided Quantum Chemistry and Machine Learning
Shanquan Wang·Guofang Xu·Shangwei Zhang·Lue Tian·Qihong Lu·Bixian Mai·Lorenz Adrian·Jan Dolfing·Haozheng He
