The foundation of polymer databases is built upon to critically examine the performance and applicability of current encoding strategies across different use cases and focuses on two major AI application domains, property prediction and inverse design, to evaluate the strengths, weaknesses and suitable scenarios for various model architectures.
Abstract
Artificial intelligence (AI) is redefining the landscape of polymer science. Although numerous AI applications have been introduced in this field, the roles of polymer encoding strategies and different applications of AI models in polymer design remain insufficiently understood. Here, we build upon the foundation of polymer databases to critically examine the performance and applicability of current encoding strategies across different use cases. We then focus on two major AI application domains, property prediction and inverse design, to evaluate the strengths, weaknesses and suitable scenarios for various model architectures. Finally, we emphasize the significance of the online platforms for promoting data accessibility and accelerating the migration from experience-based discovery toward AI-driven innovation in polymer science and engineering. Through these discussions, we aim to provide practical guidance for future research and development in AI-assisted polymer design.
Sparked by innovations in generative artificial intelligence (AI), the field of protein design has undergone a paradigm shift with an explosion of new models for optimizing existing enzymes or creating them from scratch. After more than one decade of low success rates for computationally designed enzymes, generative AI models are now frequently used for designing proficient enzymes. Here, we provide a comprehensive overview and classification of generative AI models for enzyme design, highlighting models with experimental validation relevant to real-world settings and outlining their respective limitations. We argue that generative AI models now have the maturity to create and optimize enzymes for industrial applications. Wider adoption of generative AI models with experimental feedback loops can speed up the development of biocatalysts and serve as a community assessment to inform the next generation of models.
Lasse Middendorf, Noelia Ferruz· Current Opinion in Chemical...· 1 citation
Nanotechnology and Artificial Intelligence (AI) have separately been
revolutionizing the world of scientific and industrial innovation. The combination of both has
resulted in fast material discovery, improved diagnostic abilities, intelligent therapeutic systems, and advanced computational systems. However, the current literature has presented a gap
in structured evaluations of the impact of AI methodologies on nanoscale research and the role
of nanoscale materials in the development of next-generation AI platforms
The structured review approach was adopted in this study to identify peer-reviewed
articles from prominent scientific databases (Scopus, Web of Science, IEEE Xplore, PubMed)
between 2015 and 2025. A pre-defined search strategy and inclusion-exclusion criteria were
used. The articles were screened to evaluate the comparison of AI models, nanomaterial applications, and performance trends.
The review shows that AI methods, such as Convolutional Neural Networks, Graph
Neural Networks, and Transformers, greatly improve nanoscale design, prediction, and characterization. AI-assisted nanotechnology advances the accuracy of simulations, minimizes the
number of experiments, and facilitates optimal drug delivery routes, biosensing, and material
properties prediction. On the other hand, nanoscale materials advance energy-efficient AI
hardware, neuromorphic computing, and high-performance computing infrastructure.
The results clearly show that there is a mutual technological front where AI advances nanotechnology and nanoscale materials advance energy-efficient AI hardware. The
current limitations include a lack of data, a lack of interpretability of AI models, a lack of
benchmarking standards, and the need for ethical and regulatory frameworks.
The field of AI and nanotechnology is a rapidly growing interdisciplinary area
with great potential for scientific and societal impact.
Arsheen Kaur, Malti Rani, Himali Sarangal et al.· Recent Advances in Computer...· 0 citations
This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices.
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim et al.· Advanced Intelligent Systems· 0 citations
Artificial intelligence is rapidly transforming everyday life and driving major advances across science. De novo enzyme design is an area of particular promise, with implications for medicine, biotechnology, and industry. Recent applications of AI-enhanced methodologies have yielded a range of enzymes with catalytic activities that were previously unattainable through conventional approaches. This perspective surveys emerging strategies and computational models that are redefining enzyme engineering and examines the opportunities and challenges on the path toward truly on-demand enzyme design.
Sebastian Lindner, Florence J. Hardy, Donald Hilvert· Biochemistry· 0 citations
The convergence of artificial intelligence (AI) and nanotechnology has substantially transformed the discovery, design, synthesis, characterization and biomedical application of nanomaterials. This review provides a structured overview of AI applications in nanotechnology, including data-driven nanomaterial discovery and inverse design using machine learning (ML) and generative models, optimization of nanoparticle synthesis through Bayesian optimization and self-driving laboratories, targeted drug delivery and personalized nanomedicine enabled by predictive ML models, deep learning for nanoscale imaging, spectroscopy and real-time particle tracking, AI-accelerated simulation of nanofluids and complex nanosystems, together with the emerging challenges, opportunities and future directions of AI-driven nanotechnology. We discuss the capabilities and limitations of widely used AI methods, including artificial neural networks, random forests, support vector machines, reinforcement learning, generative adversarial networks, variational autoencoders, graph neural networks and large language models, highlighting their suitability for different nanotechnology applications. In addition, the review examines key challenges that currently limit broader translation of AI-enabled nanotechnologies, including limited availability of standardized high-quality datasets, model interpretability, reproducibility, validation across independent datasets and regulatory considerations. Finally, we discuss emerging research directions, including autonomous experimentation, multiscale AI frameworks, AI-assisted nanorobotic systems and closed-loop therapeutic platforms, emphasizing that these represent promising future opportunities requiring further technological development and rigorous experimental and clinical validation.
Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial
intelligence is transforming the field, helping to predict molecular behavior, find potential designs,
and automate certain aspects of the discovery process, introducing a new kind of intuition. Over
the past decade, advances in machine learning, natural language processing, robotics, and automation
have enabled new areas of research. These are broadening the applications for retrosynthetic analysis,
reaction optimization, and computer-aided synthetic planning. This study examines the evolution of
computer-aided synthesis, describing its development from rule-based approaches to advanced deep
learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate
predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid
hypothesis generation, reaction screening, and the improvement of synthetic methods. The review
encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver
discoveries more quickly and minimize waste and environmental impact. It examines current challenges,
such as data scarcity, sporadic reporting, model interpretability, and practical applications.
More broadly, the need for sustainable, collaborative research has increased, and cross-border work
through cloud-based laboratories and shared databases enables chemists worldwide to share resources.
The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities
for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic
and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition,
bringing together research that traditional methods would never have allowed, on a scale never before
possible without AI.
Rizvee Ahmad Samir, Yu-Meng Zhang, Zi-Shan Xu et al.· Letters in Organic Chemistry· 0 citations
