Journal of Cheminformatics最新文献

{"title":"Publishing neural networks in drug discovery might compromise training data privacy","authors":"Fabian P. Krüger,&nbsp;Johan Östman,&nbsp;Lewis Mervin,&nbsp;Igor V. Tetko,&nbsp;Ola Engkvist","doi":"10.1186/s13321-025-00982-w","DOIUrl":"10.1186/s13321-025-00982-w","url":null,"abstract":"<div><p>This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess privacy risks of classification models and molecular representations, available at https://github.com/FabianKruger/molprivacy. Our findings highlight the need for careful consideration when sharing neural networks trained on proprietary chemical structures, informing organisations and researchers about the trade-offs between data confidentiality and model openness.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00982-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified approach to inferring chemical compounds with the desired aqueous solubility","authors":"Muniba Batool,&nbsp;Naveed Ahmed Azam,&nbsp;Jianshen Zhu,&nbsp;Kazuya Haraguchi,&nbsp;Liang Zhao,&nbsp;Tatsuya Akutsu","doi":"10.1186/s13321-025-00966-w","DOIUrl":"10.1186/s13321-025-00966-w","url":null,"abstract":"<div><p>Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors, multiple linear regression (MLR), and mixed integer linear programming (MILP). Selected descriptors based on a forward stepwise procedure enabled the simplest regression model, MLR, to achieve significantly good prediction accuracy compared to the existing approaches, achieving accuracy in the range [0.7191, 0.9377] for 29 diverse datasets. By simulating these descriptors and learning models as MILPs, we inferred mathematically exact and optimal compounds with the desired AS, prescribed structures, and up to 50 non-hydrogen atoms in a reasonable time range [6, 1166] seconds. These findings indicate a strong correlation between the simple graph-theoretic descriptors and the AS of compounds, potentially leading to a deeper understanding of their AS without relying on widely used complicated chemical descriptors and complex machine learning models that are computationally expensive, and therefore difficult to use for inference. An implementation of the proposed approach is available at https://github.com/ku-dml/mol-infer/tree/master/AqSol. </p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00966-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models open new way of AI-assisted molecule design for chemists","authors":"Shoichi Ishida,&nbsp;Tomohiro Sato,&nbsp;Teruki Honma,&nbsp;Kei Terayama","doi":"10.1186/s13321-025-00984-8","DOIUrl":"10.1186/s13321-025-00984-8","url":null,"abstract":"<p>Recent advancements in artificial intelligence (AI)-based molecular design methodologies have offered synthetic chemists new ways to design functional molecules with their desired properties. While various AI-based molecule generators have significantly advanced toward practical applications, their effective use still requires specialized knowledge and skills concerning AI techniques. Here, we develop a large language model (LLM)-powered chatbot, ChatChemTS, that assists users in designing new molecules using an AI-based molecule generator through only chat interactions, including automated construction of reward functions for the specified properties. Our study showcases the utility of ChatChemTS through de novo design cases involving chromophores and anticancer drugs (epidermal growth factor receptor inhibitors), exemplifying single- and multiobjective molecule optimization scenarios, respectively. ChatChemTS is provided as an open-source package on GitHub at https://github.com/molecule-generator-collection/ChatChemTS.</p><p><b>Scientific contribution</b></p><p>ChatChemTS is an open-source application that assists users in utilizing an AI-based molecule generator, ChemTSv2, solely through chat interactions. This study demonstrates that LLMs possess the potential to utilize advanced software, such as AI-based molecular generators, which require specialized knowledge and technical skills.</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00984-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable deep geometric learning model to predict the effects of mutations on protein–protein interactions using large-scale protein language model","authors":"Caiya Zhang,&nbsp;Yan Sun,&nbsp;Pingzhao Hu","doi":"10.1186/s13321-025-00979-5","DOIUrl":"10.1186/s13321-025-00979-5","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Protein–protein interactions (PPIs) are central to the mechanisms of signaling pathways and immune responses, which can help us understand disease etiology. Therefore, there is a significant need for efficient and rapid automated approaches to predict changes in PPIs. In recent years, there has been a significant increase in applying deep learning techniques to predict changes in binding affinity between the original protein complex and its mutant variants. Particularly, the adoption of graph neural networks (GNNs) has gained prominence for their ability to learn representations of protein–protein complexes. However, the conventional GNNs have mainly concentrated on capturing local features, often disregarding the interactions among distant elements that hold potential important information. In this study, we have developed a transformer-based graph neural network to extract features of the mutant segment from the three-dimensional structure of protein–protein complexes. By embracing both local and global features, the approach ensures a more comprehensive understanding of the intricate relationships, thus promising more accurate predictions of binding affinity changes. To enhance the representation capability of protein features, we incorporate a large-scale pre-trained protein language model into our approach and employ the global protein feature it provides. The proposed model is shown to be able to predict the mutation changes in binding affinity with a root mean square error of 1.10 and a Pearson correlation coefficient of near 0.71, as demonstrated by performance on test and validation cases. Our experiments on all five datasets, including both single mutant and multiple mutant cases, demonstrate that our model outperforms four state-of-the-art baseline methods, and the efficacy was subjected to comprehensive experimental evaluation. Our study introduces a transformer-based graph neural network approach to accurately predict changes in protein–protein interactions (PPIs). By integrating local and global features and leveraging pretrained protein language models, our model outperforms state-of-the-art methods across diverse datasets. The results of this study can provide new views for studying immune responses and disease etiology related to protein mutations. Furthermore, this approach may contribute to other biological or biochemical studies related to PPIs.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Scientific contribution&lt;/b&gt; Our scientific contribution lies in the development of a novel transformer-based graph neural network tailored to predict changes in protein–protein interactions (PPIs) with excellent accuracy. By seamlessly integrating both local and global features extracted from the three-dimensional structure of protein–protein complexes, and leveraging the rich representations provided by pretrained protein language models, our approach surpasses existing methods across diverse datasets. Our findings may offer novel insights for the understanding of com","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00979-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anticipating protein evolution with successor sequence predictor","authors":"Rayyan Tariq Khan,&nbsp;Pavel Kohout,&nbsp;Milos Musil,&nbsp;Monika Rosinska,&nbsp;Jiri Damborsky,&nbsp;Stanislav Mazurenko,&nbsp;David Bednar","doi":"10.1186/s13321-025-00971-z","DOIUrl":"10.1186/s13321-025-00971-z","url":null,"abstract":"<p>The quest to predict and understand protein evolution has been hindered by limitations on both the theoretical and the experimental fronts. Most existing theoretical models of evolution are descriptive, rather than predictive, leaving the final modifications in the hands of researchers. Existing experimental techniques to help probe the evolutionary sequence space of proteins, such as directed evolution, are resource-intensive and require specialised skills. We present the successor sequence predictor (SSP) as an innovative solution. Successor sequence predictor is an in silico protein design method that mimics laboratory-based protein evolution by reconstructing a protein's evolutionary history and suggesting future amino acid substitutions based on trends observed in that history through carefully selected physicochemical descriptors. This approach enhances specialised proteins by predicting mutations that improve desired properties, such as thermostability, activity, and solubility. Successor Sequence Predictor can thus be used as a general protein engineering tool to develop practically useful proteins. The code of the Successor Sequence Predictor is provided at https://github.com/loschmidt/successor-sequence-predictor, and the design of mutations will be also possible via an easy-to-use web server https://loschmidt.chemi.muni.cz/fireprotasr/.</p><p> The Successor Sequence Predictor advances protein evolution prediction at the amino acid level by integrating ancestral sequence reconstruction with a novel in silico approach that models evolutionary trends through selected physicochemical descriptors. Unlike prior work, SSP can forecast future amino acid substitutions that enhance protein properties such as thermostability, activity, and solubility. This method reduces reliance on resource-intensive directed evolution techniques while providing a generalizable, predictive tool for protein engineering.</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00971-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The specification game: rethinking the evaluation of drug response prediction for precision oncology","authors":"Francesco Codicè,&nbsp;Corrado Pancotti,&nbsp;Cesare Rollo,&nbsp;Yves Moreau,&nbsp;Piero Fariselli,&nbsp;Daniele Raimondi","doi":"10.1186/s13321-025-00972-y","DOIUrl":"10.1186/s13321-025-00972-y","url":null,"abstract":"<div><p>Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability to <i>generalize</i> over the immense space of cancer cells and chemical compounds. </p><p><b>Scientific contribution</b> In this paper we show that the commonly used evaluation strategies for DRP methods can be easily <i>fooled</i> by commonly occurring dataset biases, and they are therefore not able to truly measure the ability of DRP methods to generalize over drugs and cell lines (”specification gaming”). This problem hinders the development of reliable DRP methods and their application to experimental pipelines. Here we propose a new validation protocol composed by three Aggregation Strategies (Global, Fixed-Drug, and Fixed-Cell Line) integrating them with three of the most commonly used train-test evaluation settings, to ensure a truly realistic assessment of the prediction performance. We also scrutinize the challenges associated with using IC50 as a prediction label, showing how its close correlation with the drug concentration ranges worsens the risk of misleading performance assessment, and we indicate an additional reason to replace it with the Area Under the Dose-Response Curve instead.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00972-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fifteen years of ChEMBL and its role in cheminformatics and drug discovery","authors":"Barbara Zdrazil","doi":"10.1186/s13321-025-00963-z","DOIUrl":"10.1186/s13321-025-00963-z","url":null,"abstract":"<div><p>In October 2024 we celebrated the 15th anniversary of the first launch of ChEMBL, Europe’s most impactful, open-access drug discovery database, hosted by EMBL’s European Bioinformatics Institute (EMBL-EBI). This is a good moment to reflect on ChEMBL’s history, the role that ChEMBL plays in Cheminformatics and Drug Discovery as well as innovations accelerated using data extracted from it. The review closes by discussing current challenges and possible directions that need to be taken to guarantee that ChEMBL continues to be the pioneering resource for highly curated, open bioactivity data on the European continent and beyond.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00963-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating the inference of string generation-based chemical reaction models for industrial applications","authors":"Mikhail Andronov,&nbsp;Natalia Andronova,&nbsp;Michael Wand,&nbsp;Jürgen Schmidhuber,&nbsp;Djork-Arné Clevert","doi":"10.1186/s13321-025-00974-w","DOIUrl":"10.1186/s13321-025-00974-w","url":null,"abstract":"<div><p>Transformer-based, template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis are of interest to computer-aided synthesis planning systems, as they offer state-of-the-art accuracy. However, their slow inference speed limits their practical utility in such applications. To address this challenge, we propose speculative decoding with a simple chemically specific drafting strategy and apply it to the Molecular Transformer, an encoder-decoder transformer for conditional SMILES generation. Our approach achieves over 3X faster inference in reaction product prediction and single-step retrosynthesis with no loss in accuracy, increasing the potential of the transformer as the backbone of synthesis planning systems. To accelerate the simultaneous generation of multiple precursor SMILES for a given query SMILES in single-step retrosynthesis, we introduce Speculative Beam Search, a novel algorithm tackling the challenge of beam search acceleration with speculative decoding. Our methods aim to improve transformer-based models’ scalability and industrial applicability in synthesis planning.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00974-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143582843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability","authors":"Gregory W. Kyro,&nbsp;Matthew T. Martin,&nbsp;Eric D. Watt,&nbsp;Victor S. Batista","doi":"10.1186/s13321-025-00976-8","DOIUrl":"10.1186/s13321-025-00976-8","url":null,"abstract":"&lt;div&gt;&lt;p&gt;The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated Na&lt;sub&gt;V&lt;/sub&gt;1.5 and Ca&lt;sub&gt;V&lt;/sub&gt;1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, Na&lt;sub&gt;V&lt;/sub&gt;1.5 and Ca&lt;sub&gt;V&lt;/sub&gt;1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source at https://github.com/gregory-kyro/CardioGenAI to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Scientific contribution&lt;/b&gt;&lt;/p&gt;&lt;p&gt;This work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, Na&lt;sub&gt;V&lt;/sub&gt;1.5 and Ca&lt;sub&gt;V&lt;/sub&gt;1.2 channel activity, which can function independently as effective components of virtual screening pipelines.&lt;/p&gt;","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00976-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models","authors":"Hannah Rosa Friesacher,&nbsp;Ola Engkvist,&nbsp;Lewis Mervin,&nbsp;Yves Moreau,&nbsp;Adam Arany","doi":"10.1186/s13321-025-00964-y","DOIUrl":"10.1186/s13321-025-00964-y","url":null,"abstract":"<div><p>In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. Estimating the uncertainty inherent in these neural network predictions provides valuable information that facilitates optimal decision-making when risk assessment is crucial. However, such models can be poorly calibrated, which results in unreliable uncertainty estimates that do not reflect the true predictive uncertainty. In this study, we compare different metrics, including accuracy and calibration scores, used for model hyperparameter tuning to investigate which model selection strategy achieves well-calibrated models. Furthermore, we propose to use a computationally efficient Bayesian uncertainty estimation method named HMC Bayesian Last Layer (HBLL), which generates Hamiltonian Monte Carlo (HMC) trajectories to obtain samples for the parameters of a Bayesian logistic regression fitted to the hidden layer of the baseline neural network. We report that this approach improves model calibration and achieves the performance of common uncertainty quantification methods by combining the benefits of uncertainty estimation and probability calibration methods. Finally, we show that combining post hoc calibration method with well-performing uncertainty quantification approaches can boost model accuracy and calibration. </p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00964-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}