Aug 2026· ChemistrySelect· Vol 11· 0 citations· 164 references
TL;DR
A critical perspective is provided on how generative models are shaping the future of rational and reliable drug design, including automated synthesis planning, retrosynthesis prediction, and multi‐objective optimization.
Abstract
Generative chemistry is an emerging discipline that utilizes generative artificial intelligence (AI) models for the automated de novo design of small molecules. By learning patterns from existing chemical data, these models can generate novel structures with desired properties, thereby accelerating drug discovery. However, a significant gap remains between the potential of AI and its successful implementation in practical pharmaceutical applications. This review covers various infrastructural aspects of generative chemistry, including molecular representation, databases, and diverse model architectures such as generative adversarial networks, variational autoencoders, and diffusion models. Furthermore, key challenges associated with data quality, model selection, and synthesis feasibility are critically discussed. The review highlights that generative chemistry has evolved beyond simple structure generation to encompass the entire molecular design pipeline, including automated synthesis planning, retrosynthesis prediction, and multi‐objective optimization. Additionally, the selection of the most suitable model depends on specific objectives and the quality and diversity of the dataset, rather than a single superior architecture. Overall, a critical perspective is provided on how generative models are shaping the future of rational and reliable drug design.
ScrambleBench provides a holistic medicinal chemistry-oriented framework that identifies methodological strengths, limitations, and opportunities for future model development and highlights the importance of evaluating chemical diversity explicitly and using the recently proposed metrics such as Hamiltonian Diversity (HamDiv) which assess both quantity and dissimilarity of a molecular set.
Veincent Yap, Pan Xu, Frankie S. Mak et al.· Journal of Cheminformatics· 0 citations
This review examines how machine learning, deep learning, natural language processing (NLP), and generative modeling are being applied across medicinal chemistry and drug development, and highlights how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design.
Kaicheng U, Sophia Meixuan Zhang, Ziyu Yu et al.· Chemical Society Reviews· 0 citations
Generative models are emerging as a key technology for accelerating molecular discovery in drug design, materials science, and catalysis by enabling efficient exploration of the vast chemical space of possible molecules. Recent advances in deep generative modeling—including variational autoencoders (VAEs), diffusion models, flow matching methods, and autoregressive transformer-based approaches—have produced a diverse toolkit for generating molecular structures and optimizing their properties. However, these paradigms are often studied independently, leaving many machine learning researchers without a clear understanding of their connections, strengths, and limitations in molecular applications. This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures. While the tutorial primarily focuses on generative models for de novo molecular design, we also briefly discuss how similar modeling paradigms extend to reaction prediction and retrosynthesis. By presenting these models within a common framework, the tutorial aims to equip ML researchers and AI-for-science practitioners with a clear conceptual map of the generative modeling landscape for molecular discovery and identify emerging research opportunities in this rapidly evolving area.
Kehan Guo, Yili Shen, Jeeyhun Hwang et al.· Proceedings of the 32nd ACM...· 0 citations
A strategic roadmap for seamlessly integrating generative AI with physics-based validation with physics-based validation is provided, thereby accelerating the transition of computationally designed peptides from in silico blueprints to viable clinical candidates.
Wenjing Hu, Yuantao Sun, Ting Li et al.· The Innovation Drug Discover...· 2 citations
The growing integration of artificial intelligence (AI) and machine learning (ML) is transforming experimental chemistry laboratories. Especially in synthetic chemistry, researchers routinely handle complex and high‐dimensional data, fostering meaningful synergies between chemistry and data science. This review is intended as a practical overview that connects the everyday challenges of synthetic chemists with the digital tools available to address them. It does not seek to explain theoretical foundations of ML or to provide a comprehensive survey of all recent studies in the field. Rather, our goal is to highlight emerging technologies, discuss key considerations for their application, and present a selection of illustrative examples. To begin, we outline the prerequisites for successfully applying data science in synthetic chemistry. Next, we give a realistic overview of strategies and bottlenecks in predictive modeling of molecular properties, reaction outcomes and reaction conditions. We further highlight data‐driven approaches that can be applied in the development of new chemical reactions and synthetic methodologies, including all relevant stages from reaction discovery and optimization to substrate scope evaluation and mechanistic analyses. Finally, we briefly discuss the transformative role of large language models and agentic workflows in synthetic chemistry, focusing on opportunities and challenges in the laboratories of the future.
Generative artificial intelligence (AI) is now widely applied in medicinal chemistry, with detailed case studies emerging in the literature. Here, we describe an early application of REINVENT, AstraZeneca's in-house generative molecular design platform, to identify new inhibitor scaffolds for hematopoietic progenitor kinase 1 (HPK1). REINVENT was deployed at two stages of the project to address distinct design objectives. For hit identification, transfer learning on kinase-active compounds, followed by reinforcement learning guided by QSAR-based scoring, led to the discovery of three active chemotypes. Subsequently, REINVENT was applied to scaffold hopping, using 3D pharmacophore and docking models as scoring functions, which enabled the identification of two additional active chemotypes. Optimization of one of these scaffolds delivered a compound with potent cellular activity, kinase selectivity, and favorable rat pharmacokinetics. These results demonstrate the value of integrating generative AI with medicinal chemistry expertise and support broader application of the approach in future discovery programs.
K. Giblin, Kun Song, Hongming Chen et al.· Journal of Medicinal Chemist...· 0 citations