# Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning

arXiv: https://arxiv.org/abs/2605.13101 · HTML: https://mlsanity.com/paper/2605.13101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 19 of 19)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [FUDGE: Controlled Text Generation With Future Discriminators](https://mlsanity.com/paper/2104.05218/) (2021)
- [Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction](https://mlsanity.com/paper/1811.02633/) (2018)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Learning to Write with Cooperative Discriminators](https://mlsanity.com/paper/1805.06087/) (2018)
- [Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits](https://mlsanity.com/paper/2006.15426/) (2020)
- [Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search](https://mlsanity.com/paper/2006.15820/) (2020)
- [Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction](https://mlsanity.com/paper/2203.11444/) (2022)
- [Pragmatically Informative Image Captioning with Character-Level Inference](https://mlsanity.com/paper/1804.05417/) (2018)
- [Controllable Text Generation with Neurally-Decomposed Oracle](https://mlsanity.com/paper/2205.14219/) (2022)
- [Re-evaluating Retrosynthesis Algorithms with Syntheseus](https://mlsanity.com/paper/2310.19796/) (2023)
- [Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search](https://mlsanity.com/paper/2407.06334/) (2024)
- [Retrosynthetic Planning with Dual Value Networks](https://mlsanity.com/paper/2301.13755/) (2023)
- [Retro-fallback: retrosynthetic planning in an uncertain world](https://mlsanity.com/paper/2310.09270/) (2023)
- [RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets](https://mlsanity.com/paper/2406.18739/) (2024)
- [Tango*: Constrained synthesis planning using chemically informed value functions](https://mlsanity.com/paper/2412.03424/) (2024)

## Abstract (arXiv)

Synthesis planning seeks an efficient sequence of chemical reactions that produce a target molecule. Typically, a pretrained single-step (autoregressive) retrosynthesis model is repeatedly invoked to generate such a sequence. Classifier guidance can, in principle, help steer the output of single-step model toward reactions that satisfy specific constraints or accommodate chemist's preferences during inference without having to retrain the autoregressive generator. We expose the insufficiency of auxiliary classifiers trained with cross-entropy loss to override the unconditional token-level distributions learned from typical sparse single-disconnection reaction datasets. We overcome this issue with a novel method called Sequence Completion Ranking (SCR), which employs contrastive argumentation and a margin-based loss to calibrate the classifier so that it can meaningfully discriminate between continuations during decoding. We formally establish that margin-calibrated classifiers can expand the set of property-satisfying sequences reachable under guided beam search. Empirically, on USPTO-190, given chemist-specified guidance targets, SCR substantially improves multi-step solve rates from 16.8% (unguided generator) to 78.4% with reaction-type guidance and 95.3% with Tanimoto guidance, unlocking valid routes for 33 targets (17.4%) previously unsolvable with baselines. Our method also effectively closes the long-standing diversity gap between template-free and template-based methods.