# Generative chemistry: drug discovery with deep learning generative models

arXiv: https://arxiv.org/abs/2008.09000 · HTML: https://mlsanity.com/paper/2008.09000/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [Retrieval-based Controllable Molecule Generation](https://mlsanity.com/paper/2208.11126/) (2022)
- [Ab-initio quantum chemistry with neural-network wavefunctions](https://mlsanity.com/paper/2208.12590/) (2022)
- [Artificial Intelligence in Drug Discovery: Applications and Techniques](https://mlsanity.com/paper/2106.05386/) (2021)
- [How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules](https://mlsanity.com/paper/2112.12542/) (2021)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation](https://mlsanity.com/paper/2403.20261/) (2024)
- [MAGNet: Motif-Agnostic Generation of Molecules from Shapes](https://mlsanity.com/paper/2305.19303/) (2023)
- [The power of motifs as inductive bias for learning molecular distributions](https://mlsanity.com/paper/2306.17246/) (2023)
- [Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance](https://mlsanity.com/paper/2307.03811/) (2023)
- [Stable generative modeling using Schr"odinger bridges](https://mlsanity.com/paper/2401.04372/) (2024)
- [Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models](https://mlsanity.com/paper/2303.10774/) (2023)
- [BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design](https://mlsanity.com/paper/2405.16861/) (2024)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [SANGEA: Scalable and Attributed Network Generation](https://mlsanity.com/paper/2309.15648/) (2023)
- [How Evaluation Choices Distort the Outcome of Generative Drug Discovery](https://mlsanity.com/paper/2501.05457/) (2024)
- [Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization](https://mlsanity.com/paper/2509.25538/) (2025)
- [Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language](https://mlsanity.com/paper/2608.03855/) (2026)
- [In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation](https://mlsanity.com/paper/2204.02513/) (2022)
- [Generated Graph Detection](https://mlsanity.com/paper/2306.07758/) (2023)
- [Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic](https://mlsanity.com/paper/2507.17876/) (2025)
- [Conformal Reliability: A New Evaluation Metric for Conditional Generation](https://mlsanity.com/paper/2605.30807/) (2026)

## Abstract (arXiv)

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.