# SynCraft: Guiding Large Language Models to Predict Edit Sequences for Molecular Synthesizability Optimization

arXiv: https://arxiv.org/abs/2512.20333 · HTML: https://mlsanity.com/paper/2512.20333/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://mlsanity.com/paper/2205.07249/) (2022)
- [A 3D Generative Model for Structure-Based Drug Design](https://mlsanity.com/paper/2203.10446/) (2022)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models](https://mlsanity.com/paper/2110.15200/) (2021)
- [The Synthesizability of Molecules Proposed by Generative Models](https://mlsanity.com/paper/2002.07007/) (2020)
- [Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design](https://mlsanity.com/paper/2110.06389/) (2021)
- [RGFN: Synthesizable Molecular Generation Using GFlowNets](https://mlsanity.com/paper/2406.08506/) (2024)
- [Learning to design drug-like molecules in three-dimensional space using deep generative models](https://mlsanity.com/paper/2104.08474/) (2021)
- [Projecting Molecules into Synthesizable Chemical Spaces](https://mlsanity.com/paper/2406.04628/) (2024)
- [SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models](https://mlsanity.com/paper/2503.12602/) (2025)
- [SUPERChem: A Multimodal Reasoning Benchmark in Chemistry](https://mlsanity.com/paper/2512.01274/) (2025)
- [Exploring Synthesizable Chemical Space with Iterative Pathway Refinements](https://mlsanity.com/paper/2509.16084/) (2025)
- [How to Make Large Language Models Generate 100% Valid Molecules?](https://mlsanity.com/paper/2509.23099/) (2025)
- [Efficient and Programmable Exploration of Synthesizable Chemical Space](https://mlsanity.com/paper/2512.00384/) (2025)
- [SynTwins: A Retrosynthesis-Guided Framework for Synthesizable Molecular Analog Generation](https://mlsanity.com/paper/2507.02752/) (2025)

## Built on by (most cited first, 1 of 1)

- [El Agente Estructural: An Artificially Intelligent Molecular Editor](https://mlsanity.com/paper/2602.04849/) (2026)

## Abstract (arXiv)

Generative artificial intelligence has revolutionized the exploration of chemical space, yet a critical bottleneck remains that a substantial fraction of generated molecules is synthetically inaccessible. Current solutions, such as post-hoc filtering or projection-based methods, often compromise structural novelty or disrupt key pharmacophores by forcing molecules into pre-defined synthetic templates. Herein, we introduce SynCraft, a reasoning-based framework that reframes synthesizability optimization not as a sequence translation task, but as a precise structural editing problem. Leveraging the emergent reasoning capabilities of Large Language Models, SynCraft navigates the "synthesis cliff" where minimal structural modifications yield significant gains in synthetic feasibility. By predicting executable sequences of atom-level edits rather than generating SMILES strings directly, SynCraft circumvents the syntactic fragility of LLMs while harnessing their chemical intuition. Extensive benchmarks demonstrate that SynCraft outperforms state-of-the-art baselines in generating synthesizable analogs with high structural fidelity. Furthermore, through interaction-aware prompting, SynCraft successfully replicates expert medicinal chemistry intuition in editing PLK1 inhibitors and rescuing high-scoring but previously discarded RIPK1 candidates in previous molecular generation literatures.