A Review on AI-Driven Methods and Data Resources for Molecule Generation

doi:10.11871/jfdc.issn.2096-742X.2026.02.014

Abstract

Abstract:

[Objective] Advances in artificial intelligence (AI) and the rapid growth of molecular data have made AI-driven molecular generation a key technology in drug design and chemical innovation. This review focuses on small molecule design, summarizing AI-driven generation methods, key data resources, and their current applications in drug discovery. [Coverage] We survey key data resources that support small molecule generation, summarize representative generative modeling approaches, and analyze recent applications studies from both domestic and international research. [Methods] The article reviews mainstream deep learning architectures used in molecular generation, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, Diffusion Models, and Large Language Models (LLMs). It further summarizes advanced strategies including target-guided, fragment-based, and language model-driven generation. [Results] AI-driven small molecule generation has shown strong effectiveness in various applications. Growing model complexity and data volume require higher-quality datasets, advanced algorithms, and increased computational resources. [Limitations] This review mainly covers key data resources, core methods, and main applications in small molecule generation, without encompassing all subfields or the latest developments in this rapidly evolving area. [Conclusions] The development of domain-specific data resources has enabled a range of AI-driven small molecule generation methods, accelerating molecular discovery and drug design. Building high-quality, AI-ready datasets, improving model controllability and generalization, and refining evaluation systems remain important directions for future research.

Key words: drug design, molecule generation, deep generative models, artificial intelligence

XU Huangchao, ZHANG Baohua, LIU Qian, JIN Zhong. A Review on AI-Driven Methods and Data Resources for Molecule Generation[J]. Frontiers of Data and Computing, 2026, 8(2): 184-203, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.02.014.

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分类	数据集	数据规模	原始数据格式	访问途径
小分子数据	ZINC	37 B	SMILES、SDF	https://cartblanche22.docking.org/
	ChEMBL	22.7 M	SMILES、InChI	httpss://www.ebi.ac.uk/chembl
	PubChem	750 M	SMILES、SDF	httpss://pubchem.ncbi.nlm.nih.gov
	Enamine REAL	10 B	SMILES、SDF	httpss://www.enamine-genez.com/compound_library/real-database
	DrugBank	1.5 M	SMILES、MOL、SDF	httpss://go.drugbank.com
	ChemDiv	1.6 M	SMILES、SDF	httpss://www.chemdiv.com
蛋白质和复合物数据	RCSB PDB	238 K	Experimental PDB	httpss://www.rcsb.org
	AlphaFoldDB	200 M	Predicted PDB	httpss://alphafold.ebi.ac.uk
	UniProt	250 M	Sequence Entry	httpss://www.uniprot.org
	PDBBind	35 K	PDB、SDF	httpss://www.pdbbind-plus.org.cn
	Crossdocked	22.5 M	PDB、SDF	httpss://github.com/gnina/models/tree/master/data/CrossDocked2020
	KLIFS	6 K	PDB、SDF	httpss://klifs.net
	BindingDB	1.2 M	SMILES、Sequence	httpss://www.bindingdb.org
基准评估数据	MoleculeNet	700 K	SMILES	httpss://moleculenet.org
	MOSES	1.9 M	SMILES	httpss://github.com/molecularsets/moses
	GuacaMol	/	SMILES、MD5	httpss://github.com/BenevolentAI/guacamol
	TDC	/	SMILES、PDB	httpss://tdcommons.ai
	ADMET Lab	400 K	SMILES	httpss://admetlab3.scbdd.com