Risk-Sensitive Reward Composition for Conditional GFlowNets

Abstract

Generative Flow Networks (GFlowNets) for structure-based drug design condition on one rigid protein structure. A flexible target holds several distinct structural shapes, its conformations, each occupied for a fraction of the simulation time. Scoring a candidate against all of them raises an open question: how do K scores become one reward? The designer cannot choose arbitrarily. Populations carry simulation error, and biology dictates which conformations are deal-breakers, so a candidate that fails one is disqualified, not merely ranked lower. No standard rule captures this. We compose the reward from a conditional value-at-risk (CVaR), a worst-case score rule, and an ambiguity radius expressing distrust in the stated weights. Together, these define a family of targets, amortised by a single conditional GFlowNet. We answer whether such a sampler can be trained on fully enumerable synthetic worlds, where every error is exact rather than estimated. Pricing the tail rather than averaging moves 2-10 times more mass to candidates that pass every conformation. One network covers the family to within 0.37-2.7x the error of a perfect sampler. An exact-KL oracle, a copy trained on the true target, shows if a shortfall is the optimiser's or the architecture's. When good candidates are rare, exploration decides: injecting unseen states finds 0.987-1.000 of good regions, while reweighting visited finds 0.35-0.76.