Evolution of Molecular Generation Models in Drug Discovery

Molecular generative models (MGMs) are rapidly reshaping the landscape of drug discovery. To date, thousands of MGMs have been reported, some of which facilitate the design and optimization of molecules with the desired biological activities and pharmacological properties. In the context of drug discovery, we classify common MGMs into three primary categories: ligand-based molecular generation (LBMG), structure-based molecular generation (SBMG), and pharmacophore-based molecular generation (PBMG). This perspective discusses the principles underlying these MGMs, their evolution, and typical examples of LBMG, SBMG, and PBMG, along with their successful applications in drug discovery. Additionally, we discuss their respective advantages, limitations, and potential future research directions with the hope of promoting the continued development and application of MGMs in advancing drug discovery.

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Publication Details

Journal
Journal of Medicinal Chemistry
Published
2026-01-09
DOI
https://doi.org/10.1021/acs.jmedchem.5c03310
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Evolution of Molecular Generation Models in Drug Discovery

G. Li, Yao Xiao, Hongxiu Xu, Junlin Yu et al.
Journal of Medicinal Chemistry
Computational Drug Discovery Methods
article

Evolution of Molecular Generation Models in Drug Discovery

G. Li, Yao Xiao, Hongxiu Xu, Junlin Yu, Ben-Gui Ye, Jian Peng
article en

Abstract

Molecular generative models (MGMs) are rapidly reshaping the landscape of drug discovery. To date, thousands of MGMs have been reported, some of which facilitate the design and optimization of molecules with the desired biological activities and pharmacological properties. In the context of drug discovery, we classify common MGMs into three primary categories: ligand-based molecular generation (LBMG), structure-based molecular generation (SBMG), and pharmacophore-based molecular generation (PBMG). This perspective discusses the principles underlying these MGMs, their evolution, and typical examples of LBMG, SBMG, and PBMG, along with their successful applications in drug discovery. Additionally, we discuss their respective advantages, limitations, and potential future research directions with the hope of promoting the continued development and application of MGMs in advancing drug discovery.

Journal of Medicinal ChemistryVol. 69(2)
Chinese Academy of Sciences (CN), Sichuan University (CN), Shanghai Institute of Materia Medica (CN), Pharmaceutical Formulations (United States) (US), Institute of Medicinal Plant Development (CN), State Key Laboratory of Drug Research
Openalex Percentile: Top 91%
Computational Drug Discovery Methods
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Evolution of Molecular Generation Models in Drug Discovery — G. Li, Yao Xiao, et al. · Journal of Medicinal Chemistry (2026) | TGRS Research Map | TGRS