# Generative Models for Automatic Chemical Design

arXiv: https://arxiv.org/abs/1907.01632 · HTML: https://mlsanity.com/paper/1907.01632/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 23 of 23)

- [MARS: Markov Molecular Sampling for Multi-objective Drug Discovery](https://mlsanity.com/paper/2103.10432/) (2021)
- [Exploring Chemical Space with Score-based Out-of-distribution Generation](https://mlsanity.com/paper/2206.07632/) (2022)
- [Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces](https://mlsanity.com/paper/2111.01186/) (2021)
- [Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design](https://mlsanity.com/paper/2110.06389/) (2021)
- [Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints](https://mlsanity.com/paper/2203.14352/) (2022)
- [Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles](https://mlsanity.com/paper/2305.01754/) (2023)
- [How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules](https://mlsanity.com/paper/2112.12542/) (2021)
- [Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks](https://mlsanity.com/paper/2101.11588/) (2021)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry](https://mlsanity.com/paper/2206.00106/) (2022)
- [A Generative Diffusion Model for Amorphous Materials](https://mlsanity.com/paper/2507.05024/) (2025)
- [Automated patent extraction powers generative modeling in focused chemical spaces](https://mlsanity.com/paper/2303.08272/) (2023)
- [Diffusion Twigs with Loop Guidance for Conditional Graph Generation](https://mlsanity.com/paper/2410.24012/) (2024)
- [Enhanced sampling of robust molecular datasets with uncertainty-based collective variables](https://mlsanity.com/paper/2402.03753/) (2024)
- [Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model](https://mlsanity.com/paper/2403.13244/) (2024)
- [Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning](https://mlsanity.com/paper/2205.15510/) (2022)
- [Accelerating Antimicrobial Peptide Discovery with Latent Structure](https://mlsanity.com/paper/2212.09450/) (2022)
- [Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distance](https://mlsanity.com/paper/2510.10706/) (2025)
- [AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning](https://mlsanity.com/paper/2605.28287/) (2026)
- [Topology-Driven Generative Completion of Lacunae in Molecular Data](https://mlsanity.com/paper/2208.00063/) (2022)
- [Human-AI Co-Creation Approach to Find Forever Chemicals Replacements](https://mlsanity.com/paper/2304.05389/) (2023)
- [Molecular design method based on novel molecular representation and variational auto-encoder](https://mlsanity.com/paper/2305.01580/) (2023)
- [ReLU Networks for Exact Generation of Similar Graphs](https://mlsanity.com/paper/2604.05929/) (2026)

## Abstract (arXiv)

Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.