# A Deep Generative Model for Molecule Optimization via One Fragment Modification

arXiv: https://arxiv.org/abs/2012.04231 · HTML: https://mlsanity.com/paper/2012.04231/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 30 of 37)

- [A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language](https://mlsanity.com/paper/2209.05481/) (2022)
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- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Discrete Diffusion Schr"odinger Bridge Matching for Graph Transformation](https://mlsanity.com/paper/2410.01500/) (2024)
- [GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization](https://mlsanity.com/paper/2502.13398/) (2025)
- [Molecule Generation for Drug Design: a Graph Learning Perspective](https://mlsanity.com/paper/2202.09212/) (2022)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [Text-guided multi-property molecular optimization with a diffusion language model](https://mlsanity.com/paper/2410.13597/) (2024)
- [Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization](https://mlsanity.com/paper/2503.03503/) (2025)
- [POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization](https://mlsanity.com/paper/2509.21737/) (2025)
- [Generative Adversarial Model-Based Optimization via Source Critic Regularization](https://mlsanity.com/paper/2402.06532/) (2024)
- [Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance](https://mlsanity.com/paper/2502.06027/) (2025)
- [SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models](https://mlsanity.com/paper/2503.12602/) (2025)
- [Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models](https://mlsanity.com/paper/2308.11890/) (2023)
- [An Evaluation of Large Language Models in Bioinformatics Research](https://mlsanity.com/paper/2402.13714/) (2024)
- [mathsfG2Retro as a Two-Step Graph Generative Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2206.04882/) (2022)
- [MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization](https://mlsanity.com/paper/2604.12237/) (2026)
- [Kernel-Elastic Autoencoder for Molecular Design](https://mlsanity.com/paper/2310.08685/) (2023)
- [Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization](https://mlsanity.com/paper/2505.23987/) (2025)
- [3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design](https://mlsanity.com/paper/2204.10663/) (2022)
- [Molecule optimization via multi-objective evolutionary in implicit chemical space](https://mlsanity.com/paper/2212.08826/) (2022)
- [Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions](https://mlsanity.com/paper/2404.04224/) (2024)
- [MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning](https://mlsanity.com/paper/2505.20131/) (2025)
- [Training Text-to-Molecule Models with Context-Aware Tokenization](https://mlsanity.com/paper/2509.04476/) (2025)
- [Meta-Learning Linear Models for Molecular Property Prediction](https://mlsanity.com/paper/2509.13527/) (2025)
- [An open unified deep graph learning framework for discovering drug leads](https://mlsanity.com/paper/2301.03424/) (2022)
- [Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization](https://mlsanity.com/paper/2403.03425/) (2024)
- [Human-level molecular optimization driven by mol-gene evolution](https://mlsanity.com/paper/2406.12910/) (2024)
- [Fragment-Masked Diffusion for Molecular Optimization](https://mlsanity.com/paper/2408.09106/) (2024)

## Abstract (arXiv)

Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size; and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modifying one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement as at least 17.8% better than Modof-pipe.