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Wu T, Zhou M, Zou J, Chen Q, Qian F, Kurths J, Liu R, Tang Y. AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria. Nat Commun 2024; 15:6288. [PMID: 39060236 PMCID: PMC11282099 DOI: 10.1038/s41467-024-50533-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.
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Affiliation(s)
- Tianyu Wu
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China
| | - Min Zhou
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Jingcheng Zou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Key Laboratory for Ultrafine Materials of Ministry of Education, Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Qi Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Key Laboratory for Ultrafine Materials of Ministry of Education, Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Feng Qian
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, 14473, Germany
- Institut für Physik, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
- The Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China
| | - Runhui Liu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China.
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Key Laboratory for Ultrafine Materials of Ministry of Education, Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.
| | - Yang Tang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China.
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2
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Wang X, Zhang W, Zhang W. Dielectric Ceramics Database Automatically Constructed by Data Mining in the Literature. J Chem Inf Model 2024. [PMID: 39042485 DOI: 10.1021/acs.jcim.4c00282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Vast published dielectric ceramics literature is a natural database for big-data analysis, discovering structure-property relationships, and property prediction. We constructed a data-mining pipeline based on natural language processing (NLP) to extract property information from about 12,900 published dielectric ceramics articles and normalized more than 20 properties. The micro-F1 scores for sentence classification, named entities recognition, relation extraction (related), and relation extraction (same), are 91.6, 82.4, 91.4, and 88.3%, respectively. We demonstrated the distribution of some essential properties according to the publication years to reveal the tendency. In order to test the reliability of the data extraction, we trained an XGBoost model to predict the dielectric constant and used the SHAP module to interpret the contribution of each feature in order to identify some of the factors that determine the dielectric properties. The result shows that including Q × f in the model can increase the dielectric constant prediction accuracy. Our work can give some hints to experimentalists on their way to improve the performances of cutting-edge materials.
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Affiliation(s)
- Xiaochao Wang
- School of Integrated Circuits Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Wanli Zhang
- School of Integrated Circuits Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Wenxu Zhang
- School of Integrated Circuits Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
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3
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Huang Z, He L, Yang Y, Li A, Zhang Z, Wu S, Wang Y, He Y, Liu X. Application of machine reading comprehension techniques for named entity recognition in materials science. J Cheminform 2024; 16:76. [PMID: 38956728 PMCID: PMC11220966 DOI: 10.1186/s13321-024-00874-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 06/14/2024] [Indexed: 07/04/2024] Open
Abstract
Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to convert the sequence labeling task into a machine reading comprehension (MRC) task. MRC method effectively can solve the challenge of extracting multiple overlapping entities by transforming it into the form of answering multiple independent questions. Moreover, the MRC framework allows for a more comprehensive understanding of the contextual information and semantic relationships within materials science literature, by integrating prior knowledge from queries. State-of-the-art (SOTA) performance was achieved on the Matscholar, BC4CHEMD, NLMChem, SOFC, and SOFC-Slot datasets, with F1-scores of 89.64%, 94.30%, 85.89%, 85.95%, and 71.73%, respectively in MRC approach. By effectively utilizing semantic information and extracting nested entities, this approach holds great significance for knowledge extraction and data analysis in the field of materials science, and thus accelerating the development of material science.Scientific contributionWe have developed an innovative NER method that enhances the efficiency and accuracy of automatic entity extraction in the field of materials science by transforming the sequence labeling task into a MRC task, this approach provides robust support for constructing knowledge graphs and other data analysis tasks.
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Affiliation(s)
- Zihui Huang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Liqiang He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Yuhang Yang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Andi Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Zhiwen Zhang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Siwei Wu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Yang Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Xujie Liu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China.
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4
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Gou Y, Zhang Y, Zhu J, Shu Y. A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries. Sci Data 2024; 11:372. [PMID: 38605057 PMCID: PMC11009284 DOI: 10.1038/s41597-024-03196-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
Natural language processing techniques enable extraction of valuable information from large amounts of published literature for the application of data science and technology, i.e. machine learning in the field of materials science. Nevertheless, the automated extraction of data from full-text documents remains a complex task. We propose a document-level natural language processing pipeline for literature extraction of comprehensive information on layered cathode materials for sodium-ion batteries. The pipeline enhances entity recognition with contextual supplementary information while capturing the article structure. Finally, a heuristic multi-level relationship extraction algorithm is employed in relation extraction to extract experimental parameters and complex performance relationships respectively. We successfully extracted a comprehensive dataset containing 5265 records from 1747 documents, encompassing essential information such as chemical composition, synthesis parameters, and electrochemical properties. By implementing our pipeline, we have made significant progress in overcoming the challenges associated with data scarcity in battery informatics. The extracted datasets provide a valuable resource for further research and development in the field of layered cathode materials.
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Affiliation(s)
- Yuxiao Gou
- School of Materials Science and Engineering, Sun Yat-sen University, Guangdong, China
| | - Yiping Zhang
- School of Materials Science and Engineering, Sun Yat-sen University, Guangdong, China
| | - Jian Zhu
- School of Materials Science and Engineering, Sun Yat-sen University, Guangdong, China
| | - Yidan Shu
- School of Materials Science and Engineering, Sun Yat-sen University, Guangdong, China.
- The Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Guangdong, China.
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5
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Bonatti AF, Vozzi G, De Maria C. Enhancing quality control in bioprinting through machine learning. Biofabrication 2024; 16:022001. [PMID: 38262061 DOI: 10.1088/1758-5090/ad2189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/23/2024] [Indexed: 01/25/2024]
Abstract
Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.
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Affiliation(s)
- Amedeo Franco Bonatti
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Giovanni Vozzi
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Carmelo De Maria
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
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6
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Zhang B, Xiao H, Ye G, Song Z, Han T, Sharman E, Luo M, Cheng A, Zhu Q, Zhao H, Zhang G, Wang S, Jiang J. Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis. J Phys Chem Lett 2024; 15:212-219. [PMID: 38157213 DOI: 10.1021/acs.jpclett.3c03345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.62-0.72, 0.60-0.82, and 0.86-0.95 in precision, recall, and accuracy, respectively. We also showcase how our approach can assist robotic chemists programmed to perform research focused on double-perovskite colloidal nanocrystals, gold colloidal nanocrystals, oxygen evolution reaction catalysts, and enzyme-like catalysts by designing materials, formulations, and synthesis parameters based on data mined from 1.1 million literature references.
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Affiliation(s)
- Baicheng Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Hengyu Xiao
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guilin Ye
- Hefei JiShu Quantum Technology Co. Ltd., Hefei 230026, China
| | - Zhaokun Song
- Hefei JiShu Quantum Technology Co. Ltd., Hefei 230026, China
| | - Tiantian Han
- Hefei JiShu Quantum Technology Co. Ltd., Hefei 230026, China
| | - Edward Sharman
- Department of Neurology, University of California, Irvine, California 92697, United States
| | - Man Luo
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Aoyuan Cheng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Qing Zhu
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Guoqing Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Song Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
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7
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Toland A, Tran H, Chen L, Li Y, Zhang C, Gutekunst W, Ramprasad R. Accelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning. J Phys Chem A 2023; 127:10709-10716. [PMID: 38055927 PMCID: PMC10749451 DOI: 10.1021/acs.jpca.3c05870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023]
Abstract
Ring-opening enthalpy (ΔHROP) is a fundamental thermodynamic quantity controlling the polymerization and depolymerization of an important class of recyclable polymers, namely, those created from ring-opening polymerization (ROP). Highly accurate first-principles-based computational methods to compute ΔHROP are computationally too demanding to efficiently guide the design of depolymerizable polymers. In this work, we develop a generalizable machine-learning model that was trained on experimental measurements and reliably computed simulation results of ΔHROP (the latter provides a pathway to systematically increase the chemical diversity of the data). Predictions of ΔHROP using this machine-learning model require essentially no time while the prediction accuracy is about ∼8 kJ/mol, approaching the well-known chemical accuracy. We hope that this effort will contribute to the future development of new depolymerizable polymers.
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Affiliation(s)
- Aubrey Toland
- School
of Materials Science & Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Huan Tran
- School
of Materials Science & Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Lihua Chen
- School
of Materials Science & Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Yinghao Li
- School
of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chao Zhang
- School
of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Will Gutekunst
- School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - Rampi Ramprasad
- School
of Materials Science & Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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8
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Gilligan LPJ, Cobelli M, Taufour V, Sanvito S. A rule-free workflow for the automated generation of databases from scientific literature. NPJ COMPUTATIONAL MATERIALS 2023; 9:222. [PMID: 38666056 PMCID: PMC11041762 DOI: 10.1038/s41524-023-01171-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 11/21/2023] [Indexed: 04/28/2024]
Abstract
In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.
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Affiliation(s)
- Luke P. J. Gilligan
- School of Physics, AMBER and CRANN Institute, Trinity College, Dublin 2, Dublin, Ireland
| | - Matteo Cobelli
- School of Physics, AMBER and CRANN Institute, Trinity College, Dublin 2, Dublin, Ireland
| | - Valentin Taufour
- Department of Physics and Astronomy, University of California, Davis, CA 95616 USA
| | - Stefano Sanvito
- School of Physics, AMBER and CRANN Institute, Trinity College, Dublin 2, Dublin, Ireland
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9
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Ting JM, Tamayo-Mendoza T, Petersen SR, Van Reet J, Ahmed UA, Snell NJ, Fisher JD, Stern M, Oviedo F. Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics. Chem Commun (Camb) 2023; 59:14197-14209. [PMID: 37955165 DOI: 10.1039/d3cc04705a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.
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10
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Zeng Z, Nie YC, Ding N, Ding QJ, Ye WT, Yang C, Sun M, E W, Zhu R, Liu Z. Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology. Chem Sci 2023; 14:9360-9373. [PMID: 37712039 PMCID: PMC10498500 DOI: 10.1039/d3sc02483k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023] Open
Abstract
AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.
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Affiliation(s)
- Zheni Zeng
- Department of Computer Science and Technology, Tsinghua University Beijing China
| | - Yi-Chen Nie
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Ning Ding
- Department of Computer Science and Technology, Tsinghua University Beijing China
| | - Qian-Jun Ding
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Wei-Ting Ye
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Cheng Yang
- School of Computer Science, Beijing University of Posts and Telecommunications Beijing China
| | - Maosong Sun
- Department of Computer Science and Technology, Tsinghua University Beijing China
| | - Weinan E
- Center for Machine Learning Research and School of Mathematical Sciences, Peking University AI for Science Institute Beijing China
| | - Rong Zhu
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Zhiyuan Liu
- Department of Computer Science and Technology, Tsinghua University Beijing China
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