# DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning

arXiv: https://arxiv.org/abs/1908.07209 · HTML: https://mlsanity.com/paper/1908.07209/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design](https://mlsanity.com/paper/2403.07902/) (2024)
- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [Learning to Extend Molecular Scaffolds with Structural Motifs](https://mlsanity.com/paper/2103.03864/) (2021)
- [Learning to design drug-like molecules in three-dimensional space using deep generative models](https://mlsanity.com/paper/2104.08474/) (2021)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [C5T5: Controllable Generation of Organic Molecules with Transformers](https://mlsanity.com/paper/2108.10307/) (2021)
- [G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training](https://mlsanity.com/paper/2204.08608/) (2022)
- [3D Molecular Generation via Virtual Dynamics](https://mlsanity.com/paper/2302.05847/) (2023)
- [Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective](https://mlsanity.com/paper/2102.06045/) (2021)
- [Fast Graph Generation via Autoregressive Noisy Filtration Modeling](https://mlsanity.com/paper/2502.02415/) (2025)
- [A Reinforcement Learning-Driven Transformer GAN for Molecular Generation](https://mlsanity.com/paper/2503.12796/) (2025)
- [Are VAEs Bad at Reconstructing Molecular Graphs?](https://mlsanity.com/paper/2305.03041/) (2023)
- [Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design](https://mlsanity.com/paper/2411.01423/) (2024)
- [MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints](https://mlsanity.com/paper/2401.16771/) (2024)
- [Fragment-Masked Diffusion for Molecular Optimization](https://mlsanity.com/paper/2408.09106/) (2024)

## Abstract (arXiv)

The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores. Finally, a command line interface is created.