# t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation

arXiv: https://arxiv.org/abs/2301.01829 · HTML: https://mlsanity.com/paper/2301.01829/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 71 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [GuacaMol: Benchmarking Models for De Novo Molecular Design](https://mlsanity.com/paper/1811.09621/) (2018)
- [Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks](https://mlsanity.com/paper/2103.03212/) (2021)
- [Weisfeiler and Lehman Go Cellular: CW Networks](https://mlsanity.com/paper/2106.12575/) (2021)
- [Hierarchical Generation of Molecular Graphs using Structural Motifs](https://mlsanity.com/paper/2002.03230/) (2020)
- [Representation Learning via Invariant Causal Mechanisms](https://mlsanity.com/paper/2010.07922/) (2020)
- [Motif-based Graph Self-Supervised Learning for Molecular Property Prediction](https://mlsanity.com/paper/2110.00987/) (2021)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models](https://mlsanity.com/paper/1705.10843/) (2017)
- [SELFIES and the future of molecular string representations](https://mlsanity.com/paper/2204.00056/) (2022)
- [Learning to Extend Molecular Scaffolds with Structural Motifs](https://mlsanity.com/paper/2103.03864/) (2021)
- [Keeping it Simple: Language Models can learn Complex Molecular Distributions](https://mlsanity.com/paper/2112.03041/) (2021)
- [Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design](https://mlsanity.com/paper/2110.06389/) (2021)
- [Group SELFIES: A Robust Fragment-Based Molecular String Representation](https://mlsanity.com/paper/2211.13322/) (2022)
- [A Deep Generative Model for Fragment-Based Molecule Generation](https://mlsanity.com/paper/2002.12826/) (2020)
- [Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks](https://mlsanity.com/paper/2202.00529/) (2022)
- [The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures](https://mlsanity.com/paper/2104.10640/) (2021)
- [A generative model for molecule generation based on chemical reaction trees](https://mlsanity.com/paper/2106.03394/) (2021)

## Built on by (most cited first, 14 of 14)

- [Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees](https://mlsanity.com/paper/2407.09357/) (2024)
- [Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation](https://mlsanity.com/paper/2402.02164/) (2024)
- [Conditional Chemical Language Models are Versatile Tools in Drug Discovery](https://mlsanity.com/paper/2507.10273/) (2025)
- [Training Text-to-Molecule Models with Context-Aware Tokenization](https://mlsanity.com/paper/2509.04476/) (2025)
- [MoleCode unlocks structural intelligence in large language models](https://mlsanity.com/paper/2605.16480/) (2026)
- [Distributional Priors Guided Diffusion for Generating 3D Molecules in Low Data Regimes](https://mlsanity.com/paper/2404.00962/) (2024)
- [A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction](https://mlsanity.com/paper/2407.12152/) (2024)
- [MSCoD: An Enhanced Bayesian Updating Framework with Multi-Scale Information Bottleneck and Cooperative Attention for Structure-Based Drug Design](https://mlsanity.com/paper/2509.25225/) (2025)
- [M-GLC: Motif-Driven Global-Local Context Graphs for Few-shot Molecular Property Prediction](https://mlsanity.com/paper/2510.21088/) (2025)
- [Chemistry Integrated Language Model using Hierarchical Molecular Representation for Polymer Informatics](https://mlsanity.com/paper/2512.06301/) (2025)
- [From Tokens to Blocks: A Block-Diffusion Perspective on Molecular Generation](https://mlsanity.com/paper/2601.21964/) (2026)
- [SIGMA: Semantic Identifier Grouping for Molecular Autoregression](https://mlsanity.com/paper/2603.25062/) (2026)
- [Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generation](https://mlsanity.com/paper/2609.13519/) (2026)
- [STAMP as a topology-aware molecular language for fragment-level learning and editing](https://mlsanity.com/paper/2610.18438/) (2026)

## Abstract (arXiv)

Effective representation of molecules is a crucial factor affecting the performance of artificial intelligence models. This study introduces a flexible, fragment-based, multiscale molecular representation framework called t-SMILES (tree-based SMILES) with three code algorithms: TSSA, TSDY and TSID. It describes molecules using SMILES-type strings obtained by performing a breadth-first search on a full binary tree formed from a fragmented molecular graph. Systematic evaluations using JTVAE, BRICS, MMPA, and Scaffold show the feasibility of constructing a multi-code molecular description system, where various descriptions complement each other, enhancing the overall performance. In addition, it can avoid overfitting and achieve higher novelty scores while maintaining reasonable similarity on labeled low-resource datasets, regardless of whether the model is original, data-augmented, or pre-trained then fine-tuned. Furthermore, it significantly outperforms classical SMILES, DeepSMILES, SELFIES and baseline models in goal-directed tasks. And it surpasses state-of-the-art fragment, graph and SMILES based approaches on ChEMBL, Zinc, and QM9.