ParetoGen: Generative Machine Learning Models To Push the Pareto Optimal Frontier of Functionality-Hazard Trade-offs in Per- and Polyfluoroalkyl Substances Green Alternative Designs

Faced with chemical pollution that has exceeded the planetary
boundaries,
endeavors must be directed toward designing green alternatives to
reduce the hazards of industrial chemicals. Per- and polyfluoroalkyl
substances (PFASs) are critical industrial chemicals with superior
functionalities but proven hazards. Trade-offs between functionalities
and hazards constitute the challenge in PFAS green substitution, known
as multiparameter optimization. The essential goal of multiparameter
optimization is discerning chemicals for which enhancements in one
property invariably lead to degradations of others. In statistical
parlance, the chemicals constitute the Pareto optimal frontiers (POFs).
Based on generative machine learning algorithms, this study developed
a method named ParetoGen for propelling POFs of multiobjective optimization
issues in green alternative designs. Results demonstrate the ParetoGen
stably and significantly pushed the POFs of functionality-hazard trade-offs
in the design of green alternatives to PFASs. Compared with existing
PFAS alternatives, structures generated by the ParetoGen exhibit lower
hazards and enhanced functionalities. By introducing the Pareto optimization
theory into the molecular design of green alternatives, this study
establishes a universal theoretical foundation and evaluation methodology
for green chemical substitution. The findings reveal valuable insights
into the chemical structural design of PFASs, and the developed method
can be employed in general chemical structural design tasks.