Inverse Protein Folding (IPF) is a critical subtask in the field of protein design, aiming to engineer amino acid sequences capable of folding correctly into a specified three-dimensional (3D) conformation. Although substantial progress has been achieved in recent years, existing methods generally rely on either backbone coordinates or molecular surface features alone, which restricts their ability to fully capture the complex chemical and geometric constraints necessary for precise sequence prediction. To address this limitation, we present DS-ProGen, a dual-structure deep language model for functional protein design, which integrates both backbone geometry and surface-level representations. By incorporating backbone coordinates as well as surface chemical and geometric descriptors into a next-amino-acid prediction paradigm, DS-ProGen is able to generate functionally relevant and structurally stable sequences while satisfying both global and local conformational constraints. On the PRIDE dataset, DS-ProGen attains the current state-of-the-art recovery rate of 61.47%, demonstrating the synergistic advantage of multi-modal structural encoding in protein design. Furthermore, DS-ProGen excels in predicting interactions with a variety of biological partners, including ligands, ions, and RNA, confirming its robust functional retention capabilities.

Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The widespread adoption of high-throughput sequencing technologies and multi-omics approaches has led to rapid accumulation of genomic, transcriptomic, proteomic, and even single-cell multimodal datasets, resulting in an exponential growth of biological data. The massive scale and inherent complexity of these datasets pose significant challenges for data management, analysis, and interpretation in the field of bioinformatics. Concurrently, artificial intelligence (AI) techniques, particularly deep learning and reinforcement learning, have achieved groundbreaking advances in medical diagnostics, drug discovery, and genomic analyses, providing novel theoretical tools and analytical paradigms for bioinformatics research. AI techniques are now extensively applied to DNA, RNA, and protein sequence prediction and design, 3D structural elucidation, functional annotation, integrative analysis of multi-omics data, and personalized drug design for precision medicine, significantly advancing biological research. This review systematically summarizes recent research progress and representative applications of AI techniques in bioinformatics, specifically discussing suitable scenarios and advantages of traditional machine learning algorithms, deep learning models, and reinforcement learning methods. We highlight AI's transformative impact with quantitative metrics from landmark achievements: accurate near-atomic protein structure prediction (median 0.96 Å on CASP14), robust single-cell modeling (AvgBIO $\approx $ 0.82), high protein design success rates (up to 92%), and sensitive cancer detection (Area Under Curve (AUC) $\approx $ 0.93). Furthermore, the paper provides an in-depth analysis of the latest advancements of AI in specific tasks, including biomedical text mining, multimodal omics integration, and single-cell analyses, while highlighting current challenges such as data noise and sparsity, difficulties in modeling long biological sequences, complexities in multimodal data integration, insufficient model interpretability, and ethical and privacy concerns. Finally, the paper outlines promising future research directions, emphasizing large-scale data mining, cross-domain model generalization, innovations in drug design and personalized medicine, and advocates for establishing an open and collaborative research ecosystem.