MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
By Yuanqi Du, Tianfan Fu, Jimeng Sun, Shengchao Liu. 2022
105 citations recorded by the source corpus.
Molecule design is a fundamental problem in molecular science and has critical applications in a variety of areas, such as drug discovery, material science, etc. However, due to the large searching space, it is impossible for human experts to enumerate and test all molecules in wet-lab experiments. Recently, with the rapid development of machine learning methods, especially generative methods, molecule design has achieved great progress by leveraging machine learning models to generate candidate molecules. In this paper, we systematically review the most relevant work in machine learning models for molecule design. We start with a brief review of the mainstream molecule featurization and representation methods (including 1D string, 2D graph, and 3D geometry) and general generative methods (deep generative and combinatorial optimization methods). Then we summarize all the existing molecule design problems into several venues according to the problem setup, including input, output types and goals. Finally, we conclude with the open challenges and point out future opportunities of machine learning models for molecule design in real-world applications.
Subfields: Computational Theory and Mathematics, Environmental Chemistry, Materials Chemistry
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Tommi S. Jaakkola
- Regina Barzilay
- Tianfan Fu
- Octavian-Eugen Ganea
- Hannes Stärk
- Lagnajit Pattanaik
- Wenqi Wei
- Wengong Jin
In Environmental Chemistry
In Materials Chemistry
- Tommi S. Jaakkola
- Regina Barzilay
- Tianfan Fu
- Octavian-Eugen Ganea
- Lagnajit Pattanaik
- Hannes Stärk
- Wengong Jin
- José Miguel Hernández-Lobato
Citations in the corpus
- EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “, 2021) and fast generation of small molecule low-energy conformation ensembles (Ganea et al., 2021b; Luo et al., 2021; Xu et al., 2020; Shi et al., 2021; Du et al., 2022).”
- Structure-based drug design with equivariant diffusion models (2022, unknown): “Another line of work focuses on generative design of new molecules [72].”
- Machine Learning for Synthetic Data Generation: a Review (2023, background): “Such capabilities can increase both the efficiency and scope of the study as well as reduce the likelihood of biases being introduced into the results [69], [98], [99].”