# Scaffold-constrained molecular generation

arXiv: https://arxiv.org/abs/2009.07778 · HTML: https://mlsanity.com/paper/2009.07778/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 18 of 18)

- [Learning to Extend Molecular Scaffolds with Structural Motifs](https://mlsanity.com/paper/2103.03864/) (2021)
- [Gotta be SAFE: A New Framework for Molecular Design](https://mlsanity.com/paper/2310.10773/) (2023)
- [Retrieval-based Controllable Molecule Generation](https://mlsanity.com/paper/2208.11126/) (2022)
- [DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization](https://mlsanity.com/paper/2403.13829/) (2024)
- [C5T5: Controllable Generation of Organic Molecules with Transformers](https://mlsanity.com/paper/2108.10307/) (2021)
- [Lo-Hi: Practical ML Drug Discovery Benchmark](https://mlsanity.com/paper/2310.06399/) (2023)
- [Deep Lead Optimization: Leveraging Generative AI for Structural Modification](https://mlsanity.com/paper/2404.19230/) (2024)
- [Many-Shot In-Context Learning for Molecular Inverse Design](https://mlsanity.com/paper/2407.19089/) (2024)
- [GraphXForm: Graph transformer for computer-aided molecular design](https://mlsanity.com/paper/2411.01667/) (2024)
- [SAFE setup for generative molecular design](https://mlsanity.com/paper/2410.20232/) (2024)
- [A Reinforcement Learning-Driven Transformer GAN for Molecular Generation](https://mlsanity.com/paper/2503.12796/) (2025)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design](https://mlsanity.com/paper/2411.01423/) (2024)
- [Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance](https://mlsanity.com/paper/2604.24474/) (2026)
- [Genetic Algorithm for Constrained Molecular Inverse Design](https://mlsanity.com/paper/2112.03518/) (2021)
- [Topology-Driven Generative Completion of Lacunae in Molecular Data](https://mlsanity.com/paper/2208.00063/) (2022)
- [Cross-Modality Controlled Molecule Generation with Diffusion Language Model](https://mlsanity.com/paper/2508.14748/) (2025)
- [HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields](https://mlsanity.com/paper/2510.18122/) (2025)

## Abstract (arXiv)

One of the major applications of generative models for drug Discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de-novo drug design. To tackle this issue, we introduce a new algorithm to perform scaffold-constrained in-silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement Learning methods, allowing to design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and, finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.