Navigating Multiobjective Drug Discovery Trade-offs with Preference-Gated GFlowNets

Multi-objective molecular generation faces a double challenge: finding molecules that survive demanding screens and changing what is sampled when a scientist changes priorities. A broad Pareto frontier alone does not establish such control. We introduce PrefGateGFN, a GFlowNet that uses preference-dependent feature gates to modulate molecular construction-action scores. Gates can change action rankings, while optional learned temperatures adjust sampling concentration; dynamic reward normalization addresses coverage as a separate training choice. We examine these roles using exact-grid distributions, matched molecular controls, and preference interventions on frozen policies. In a three-objective fragment task, the full system achieves higher mean top-10 reward, diversity, and hypervolume than the evaluated MOGFN and HN-GFN systems. A three-seed ablation raises mean hypervolume from 0.668 with normalization and curriculum disabled to 0.885 with normalization alone. In a comparison of complete systems with different training recipes, PrefGateGFN achieves substantial yield under a demanding fourteen-objective screen across five protein targets: an average of 133 distinct molecules per 1,000 draws satisfying docking, physicochemical, and twelve predicted ADMET criteria, outperforming MOGFN-PC by factors of 1.93 in qualifying molecules and 2.10 in qualifying scaffolds. Together with its measured preference response, these results support steering molecular generation toward diverse, computationally screened candidates for experimental follow-up.