Nature reviews. Chemistry最新文献_第3页

Caging carbon for chemistry 化学用的碳笼

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-08-01 DOI: 10.1038/s41570-025-00746-y

Hyuenwoo Yang, Seoyeon Kim, Pablo Fernandez

引用次数: 0

Protein–ligand data at scale to support machine learning 大规模的蛋白质配体数据支持机器学习。

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-23 DOI: 10.1038/s41570-025-00737-z

Aled M. Edwards, Dafydd R. Owen, The Structural Genomics Consortium Target 2035 Working Group

{"title":"Protein–ligand data at scale to support machine learning","authors":"Aled M. Edwards, Dafydd R. Owen, The Structural Genomics Consortium Target 2035 Working Group","doi":"10.1038/s41570-025-00737-z","DOIUrl":"10.1038/s41570-025-00737-z","url":null,"abstract":"Target 2035 is a global initiative that aims to develop a potent and selective pharmacological modulator, such as a chemical probe, for every human protein by 2035. Here, we describe the Target 2035 roadmap to develop computational methods to improve small-molecule hit discovery, which is a key bottleneck in the discovery of chemical probes. Large, publicly available datasets of high-quality protein–small-molecule binding data will be created using affinity-selection mass spectrometry and DNA-encoded chemical library screening. Positive and negative data will be made openly available, and the machine learning community will be challenged to use these data to build models and predict new, diverse small-molecule binders. Iterative cycles of prediction and testing will lead to improved models and more successful predictions. By 2030, Target 2035 will have identified experimentally verified hits for thousands of human proteins and advanced the development of open-access algorithms capable of predicting hits for proteins for which there are not yet any experimental data. Target 2035 aims to develop a potent and selective pharmacological modulator for every human protein by 2035 with the results made publicly available. This Roadmap article sets out how that will be achieved.","PeriodicalId":18849,"journal":{"name":"Nature reviews. Chemistry","volume":"9 9","pages":"634-645"},"PeriodicalIF":51.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41570-025-00737-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The power of triplets 三胞胎的力量

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-21 DOI: 10.1038/s41570-025-00744-0

Sabine Müller

引用次数: 0

Developing machine learning for heterogeneous catalysis with experimental and computational data 利用实验和计算数据开发多相催化的机器学习。

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-18 DOI: 10.1038/s41570-025-00740-4

Carlota Bozal-Ginesta, Sergio Pablo-García, Changhyeok Choi, Albert Tarancón, Alán Aspuru-Guzik

{"title":"Developing machine learning for heterogeneous catalysis with experimental and computational data","authors":"Carlota Bozal-Ginesta, Sergio Pablo-García, Changhyeok Choi, Albert Tarancón, Alán Aspuru-Guzik","doi":"10.1038/s41570-025-00740-4","DOIUrl":"10.1038/s41570-025-00740-4","url":null,"abstract":"Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyse trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R2 values, we compare the performances based on these mentioned trends. Machine learning aids heterogeneous catalysis research by linking performance to physicochemical controllable properties. This Review discusses experimental and computational high-throughput and machine learning approaches, comparing them by modelling method, features, dataset size, accuracy and reaction type.","PeriodicalId":18849,"journal":{"name":"Nature reviews. Chemistry","volume":"9 9","pages":"601-616"},"PeriodicalIF":51.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Publisher Correction: Chemistry advances driving industrial carbon capture technologies 出版者更正：化学进步推动工业碳捕获技术。

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-15 DOI: 10.1038/s41570-025-00748-w

Jeannie Z. Y. Tan, Joao M. Uratani, Steve Griffiths, John M. Andresen, M. Mercedes Maroto-Valer

引用次数: 0

Chiral medium-sized rings beyond central chirality 超越中心手性的手性中型环

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-14 DOI: 10.1038/s41570-025-00735-1

Shiqi Jia , Yudong Hao , Yuedi Li , Yu Lan

{"title":"Chiral medium-sized rings beyond central chirality","authors":"Shiqi Jia \u0000 , Yudong Hao \u0000 , Yuedi Li \u0000 , Yu Lan","doi":"10.1038/s41570-025-00735-1","DOIUrl":"10.1038/s41570-025-00735-1","url":null,"abstract":"Chiral medium-sized rings (MSRs), cyclic molecular structures comprising 7–11-membered rings, are prevalent in bioactive molecules owing to their unique three-dimensional structures and pharmacological properties. Compared with the extensively studied central chirality, MSRs with unconventional chirality — that is, axial chirality, inherent chirality and planar chirality — remain underexplored. The past decade has witnessed rapid advances in this field, with breakthroughs in their synthesis and applications. This Review is structured around the three underexplored types of chirality exhibited in MSRs detailing their key synthetic strategies, with a critical evaluation of their advantages and limitations. Additionally, the factors that influence the conformational stability of chiral MSRs are discussed using structure and energy analysis. Chiral medium-sized rings (MSRs) beyond central chirality, with unique rigid–flexible features, show broad potential. This Review highlights progress in the enantioselective synthesis of axial, inherent and planar chiral MSRs, and it critically assesses key synthetic strategies.","PeriodicalId":18849,"journal":{"name":"Nature reviews. Chemistry","volume":"9 9","pages":"617-633"},"PeriodicalIF":51.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Li-ion battery industry and its challenges 锂离子电池工业及其挑战

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-11 DOI: 10.1038/s41570-025-00742-2

Xuezhi Yang, Haiyan Zhang, Qian Liu, Guibin Jiang

引用次数: 0

Pursuing spatiotemporal coordination in electrocatalysis 追求电催化的时空协调

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-11 DOI: 10.1038/s41570-025-00741-3

Yuanfu Ren, Kuo-Wei Huang, Huabin Zhang

引用次数: 0

Developing a tool for teaching chemistry to people with visual impairments 开发一种工具来教有视觉障碍的人化学。

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-10 DOI: 10.1038/s41570-025-00743-1

Cesar Horna-Saldaña, Xavi Canaleta, Juan Ernesto Perez-Perez

引用次数: 0

A story of chance and collaboration 一个关于机遇和合作的故事

IF 51.7 1区化学

Nature reviews. Chemistry Pub Date : 2025-07-07 DOI: 10.1038/s41570-025-00736-0

Dek N. Woolfson, Stephanie Greed

引用次数: 0