Year: 2025
Type: article
Abstract: Artificial intelligence (AI) is revolutionising chemical research by significantly enhancing automated experimental processes, reaction prediction, and molecular design. Despite these advances, problems with data quality, computational resource limitations, and model interpretability persist. In order to further speed up chemical discoveries and advances, prospects include creating hybrid AI models, quantum AI, and multimodal frameworks. Using artificial intelligence (AI) to significantly improve automated experimental procedures, reaction prediction, and molecular design is revolutionising chemical research. Creating hybrid AI models, quantum AI and multimodal frameworks is a potential future avenue to accelerate chemical discoveries and further advances. Chemical research is being revolutionised by artificial intelligence (AI), which is significantly enhancing automated experimental procedures, reaction prediction, and molecular design. Recent advances in generative AI methods, such as diffusion models, GANs, and variational autoencoders (VAEs) that aid in creating unique molecular structures, are the main focus of this review effort. To increase the precision of reaction predictions, transformer-based designs and graph neural networks (GNNs) are being investigated. Some challenges remain, including low-quality data, a lack of processing capacity, and concerns over the model’s interpretability. The creation of hybrid AI models, quantum AI, and multimodal frameworks, among other exciting study topics, could accelerate future developments in chemistry.
Authors: Amit Kumar Mishra, Yonglan Kan
Institution: Aalborg University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409258266