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Molecular generative model based on conditional variational autoencoder for de novo molecular design

arXiv 1806.05805 ↗First posted 15 Jun 2018cs.LG stat.MLcited by 73

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Abstract (arXiv)

We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.

Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jun 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 12; 2022: 19; 2023: 16; 2024: 15; 2025: 8; 2026: 3.

73 papers.

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  1. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  2. 2022Retrieval-based Controllable Molecule Generationcited by 44
  3. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  4. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  5. 2022Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Modelcited by 25
  6. 2022Graph Neural Networks for Moleculescited by 21
  7. 2022Deep Reinforcement Learning for Inverse Inorganic Materials Designcited by 19
  8. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  9. 2022Target-aware Molecular Graph Generationcited by 18
  10. 2023Counterfactual Generative Models for Time-Varying Treatmentscited by 17
  11. 2023Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modelingcited by 17
  12. 2021C5T5: Controllable Generation of Organic Molecules with Transformerscited by 16
38 more
  1. 2022MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencodercited by 16
  2. 2024A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Sciencecited by 14
  3. 2023Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problemscited by 13
  4. 2023Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Modelscited by 12
  5. 2022Modular Flows: Differential Molecular Generationcited by 11
  6. 2024LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Modelscited by 11
  7. 2024Text-guided multi-property molecular optimization with a diffusion language modelcited by 11
  8. 2024Deep Lead Optimization: Leveraging Generative AI for Structural Modificationcited by 10
  9. 2023Learning from small data sets: Patch-based regularizers in inverse problems for image reconstructioncited by 9
  10. 2021Polygrammar: Grammar for Digital Polymer Representation and Generationcited by 8
  11. 2022Conditional β-VAE for De Novo Molecular Generationcited by 8
  12. 2022Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoderscited by 8
  13. 2023ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generationcited by 8
  14. 2024A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle acceleratorscited by 8
  15. 2023PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embeddingcited by 7
  16. 2021Augmenting Molecular Deep Generative Models with Topological Data Analysis Representationscited by 6
  17. 2022Graph Machine Learning for Design of High-Octane Fuelscited by 6
  18. 2023Coupled Variational Autoencodercited by 6
  19. 2023Language models in molecular discoverycited by 6
  20. 2023LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Designcited by 6
  21. 2022De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learningcited by 4
  22. 2023GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discoverycited by 4
  23. 2024A novel molecule generative model of VAE combined with Transformer for unseen structure generationcited by 4
  24. 2021Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Modelcited by 3
  25. 2022Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterialscited by 3
  26. 2024Overcoming Order in Autoregressive Graph Generationcited by 3
  27. 2025Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Modelcited by 3
  28. 2025Recent Developments in GNNs for Drug Discoverycited by 3
  29. 2023Kernel-Elastic Autoencoder for Molecular Designcited by 2
  30. 2023Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cellscited by 2
  31. 2024Unsupervised Multiple Domain Translation through Controlled Disentanglement in Variational Autoencodercited by 2
  32. 2025MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Designcited by 2
  33. 2025Morphology-Aware Peptide Discovery via Masked Conditional Generative Modelingcited by 2
  34. 2021CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modellingcited by 1
  35. 2021Deep Denerative Models for Drug Design and Responsecited by 1
  36. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  37. 2021De Novo Molecular Generation with Stacked Adversarial Modelcited by 1
  38. 2022Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspectivecited by 1

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