Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language
https://www.nature.com/articles/s42256-026-01250-8
Publish Date: 2026-06-11 05:50:00
Source Domain: www.nature.com
- Large language models are increasingly utilized in scientific research, expanding their applications from text generation to complex scientific predictions, as discussed in works by Xu et al. and Wang et al.
- The paper by Chang et al. provides a comprehensive survey on the evaluation of large language models, emphasizing their robustness and performance across various tasks.
- Burton et al. highlight the transformative role large language models can play in reshaping collective intelligence, while Demszky et al. focus on their utility in psychological research.
- Notably, the introduction of pretrained language models, as described by Wang et al., has spurred significant advances in numerous scientific domains.
- Recent advancements are seen in the areas of evolutionary biology, protein structure prediction, and drug discovery, as evidenced by works from Hayes et al., Lin et al., and Abramson et al.
- Several studies propose novel methods for molecular conformation generation and drug design, leveraging deep learning techniques, including works by Zhou et al., Chen et al., and Ross et al.
- The use of 3D molecular representations and geometric deep learning models for tasks like molecular docking, regression, and generation is explored in studies by Bagal et al., Luo et al., and Zhang et al.
- Challenges and limitations of language models in specific scientific domains, such as arithmetic and symbolic induction, are discussed by Qian et al. and Zhang et al.
- Significant efforts are dedicated to improving the quality and utility of large language models in drug discovery and molecular biology, with contributions from researchers like Zhou et al., Alakhdar et al., and Feng et al.
