# MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation

arXiv: https://arxiv.org/abs/2604.08575 · HTML: https://mlsanity.com/paper/2604.08575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation](https://mlsanity.com/paper/2001.09382/) (2020)
- [Hierarchical Generation of Molecular Graphs using Structural Motifs](https://mlsanity.com/paper/2002.03230/) (2020)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Exploring Chemical Space with Score-based Out-of-distribution Generation](https://mlsanity.com/paper/2206.07632/) (2022)
- [Enhancing Generative Models via Quantum Correlations](https://mlsanity.com/paper/2101.08354/) (2021)
- [Quantum Generative Models for Small Molecule Drug Discovery](https://mlsanity.com/paper/2101.03438/) (2021)
- [A Deep Generative Model for Fragment-Based Molecule Generation](https://mlsanity.com/paper/2002.12826/) (2020)
- [Fragment-based Pretraining and Finetuning on Molecular Graphs](https://mlsanity.com/paper/2310.03274/) (2023)
- [3D Graph Contrastive Learning for Molecular Property Prediction](https://mlsanity.com/paper/2208.06360/) (2022)
- [Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery](https://mlsanity.com/paper/2112.12563/) (2021)
- [Application of quantum-inspired generative models to small molecular datasets](https://mlsanity.com/paper/2304.10867/) (2023)
- [Accelerating the inference of string generation-based chemical reaction models for industrial applications](https://mlsanity.com/paper/2407.09685/) (2024)
- [Quantum Machine Learning: Quantum Kernel Methods](https://mlsanity.com/paper/2405.01780/) (2024)
- [Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation](https://mlsanity.com/paper/2509.09451/) (2025)
- [HingeRLC-GAN: Combating Mode Collapse with Hinge Loss and RLC Regularization](https://mlsanity.com/paper/2503.19074/) (2025)
- [Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation](https://mlsanity.com/paper/2506.01177/) (2025)
- [ConQuER: Modular Architectures for Control and Bias Mitigation in IQP Quantum Generative Models](https://mlsanity.com/paper/2509.22551/) (2025)

## Abstract (arXiv)

Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these objectives. We present MOLPAQ, a modular quantum-classical generator that assembles molecules from quantum-generated latent patches. A beta-VAE pretrained on QM9 learns a chemically aligned latent manifold; a reduced conditioner maps molecular descriptors into this space; and a parameter-efficient quantum patch generator produces entangled node embeddings that a valence-aware aggregator reconstructs into valid molecular graphs. Adversarial fine-tuning with a latent critic and chemistry-shaped reward yields 100% RDKit validity, 99.75% novelty, and 0.905 diversity. Beyond aggregate metrics, the pretrained quantum generator, steered by the conditioner, improves mean QED by approx. 2.3% and increases aromatic motif incidence by approx. 10-12% relative to a parameter-matched classical generator, highlighting its role as a compact topology-shaping operator.