Keynote #4: Preference-Guided Multi-Objective Optimization for Scientific Discovery

Multi-objective optimization problems pervade science and engineering, requiring decision-makers to select Pareto-optimal solutions aligned with their preferences among competing objectives. This challenge is particularly acute in applications like drug discovery and clinical planning, where experts must manually evaluate numerous candidates based on chemical intuition across multiple properties. We propose two applications that combine preferential multi-objective optimization with intuitive constraint specification. Our approach enables domain experts to guide the selection process through both pairwise comparisons and by defining bounds on objectives, effectively capturing domain expertise while efficiently navigating vast solution spaces.  For brachytherapy planning, our method yields solutions with over 3% greater utility than competing approaches. In drug discovery, our approach significantly outperforms state-of-the-art screening methods, recovering up to 43% of known drugs while examining only 6% of a 100K candidate library for EGFR and DRD2 targets. These results highlight the potential of preference-guided multi-objective optimization to dramatically improve efficiency in resource-intensive tasks by effectively leveraging domain expertise through intuitive preference specification.