Black Box Recursive Translations for Molecular Optimization
This provides a simple, interpretable drop-in method for drug discovery that boosts performance over non-recursive approaches, though it is incremental as it builds on existing translation models.
The authors tackled molecular optimization by framing it as a translation problem and introduced Black Box Recursive Translation (BBRT), a method that iteratively feeds generated molecules back into any translation model to improve biochemical properties, achieving new state-of-the-art results.
Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step. We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm. We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization. This simple, powerful technique operates strictly on the inputs and outputs of any translation model. We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models. Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "for" loop. Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.