.png){kind=icon}
返回
Chemical language modeling with structured state space sequence models
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-024-50469-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Generative deep learning is reshaping drug design. Chemical language models (CLMs) - which generate molecules in the form of molecular strings - bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can 'emerge' from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling - uncovering its untapped potential in the molecular sciences. Artificial Intelligence (AI) is accelerating drug discovery. Here the authors introduce a new approach to de novo molecule design - structured state space sequence models - to further extend AI's capabilities of charting the chemical universe.
Keyword:
DE-NOVO DESIGN
NATURAL-PRODUCTS
DRUG DISCOVERY
TRANSFORMER
GENERATION
INHIBITORS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
Chemical language models enable navigation in sparsely populated chemical space化学语言模型可以在稀疏的化学空间中导航
PREAI
分享收藏AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python BindingsAutoDock Vina 1.2.0: 新的对接方法，扩展的力场和Python绑定OAAI
OAAI分享收藏CHARMM-GUI Input Generator for NAMD, Gromacs, Amber, Openmm, and CHARMM/OpenMM Simulations using the CHARMM36 Additive Force Field.png)
BIOPHYSICAL JOURNALOAAI
BIOPHYSICAL JOURNALIF3.1
OAAI分享收藏
The art and practice of structure-based drug design: A molecular modeling perspective基于结构的药物设计的艺术与实践: 分子建模视角
PREAI
分享收藏Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity具有3D复杂性的大分子结构的基于变分自动编码器的化学潜空间OAAI
OAAI分享收藏
查看更多内容