Artificial intelligence chemistry最新文献_第3页

Machine learning for active sites prediction of quinoline derivatives 喹啉衍生物活性位点预测的机器学习

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2024-12-04 DOI: 10.1016/j.aichem.2024.100082

Jie Sun, Zi-Hao Li, Yi-Fei Yang, Shu-Yu Zhang

引用次数: 0

Erratum regarding missing statements in previously published article 关于先前发表的文章中缺失陈述的勘误

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2024-12-04 DOI: 10.1016/j.aichem.2024.100081

引用次数: 0

YieldFCP: Enhancing Reaction Yield Prediction via Fine-grained Cross-modal Pre-training YieldFCP：通过细粒度交叉模态预训练增强反应产率预测

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2025-03-01 DOI: 10.1016/j.aichem.2025.100085

Runhan Shi, Gufeng Yu, Letian Chen, Yang Yang

{"title":"YieldFCP: Enhancing Reaction Yield Prediction via Fine-grained Cross-modal Pre-training","authors":"Runhan Shi, Gufeng Yu, Letian Chen, Yang Yang","doi":"10.1016/j.aichem.2025.100085","DOIUrl":"10.1016/j.aichem.2025.100085","url":null,"abstract":"<div><div>Predicting chemical reaction yields is a critical yet challenging task in organic chemistry. While integrating multi-modal information has shown promise, existing methods typically encode the entire reaction in different modalities and then align these embeddings for the same reactions. Such a coarse-grained modal fusion strategy may neglect atomic-level interactions crucial for accurate predictions. Recognizing the crucial role of modal fusion in multi-modal learning and the limitations of current methods in real-world scenarios, we propose YieldFCP, a reaction <span><math><munder><mrow><mtext>Yield</mtext></mrow><mo>̲</mo></munder></math></span> prediction model based on <span><math><munder><mrow><mtext>F</mtext></mrow><mo>̲</mo></munder></math></span>ine-grained <span><math><munder><mrow><mtext>C</mtext></mrow><mo>̲</mo></munder></math></span>ross-modal <span><math><munder><mrow><mtext>P</mtext></mrow><mo>̲</mo></munder></math></span>re-training. Its cross-modal projector links the molecular SMILES sequence with 3D geometric data, focusing on the atomic-level interactions to achieve fine-grained modal fusion and enhance yield prediction. YieldFCP is pre-trained on a large-scale dataset leveraging cross-modal self-supervised learning techniques. Experimental results on the high-throughput experiments, real-world electronic laboratory notebook, and real-world organic reaction publication datasets demonstrate the effectiveness of our approach. Particularly, YieldFCP outperforms the state-of-the-art methods in real-world scenarios and successfully recognizes key components that determine reaction yields with valuable interpretability.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 1","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating eco-friendly ionic liquids with enhanced CO2 solubility using language models 使用语言模型生成具有增强二氧化碳溶解度的环保离子液体

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2025-05-22 DOI: 10.1016/j.aichem.2025.100089

Adroit T.N. Fajar , Guillaume Lambard , Md. Amirul Islam , Bidyut B. Saha , Zakiah D. Nurfajrin , Kevin Septioga

{"title":"Generating eco-friendly ionic liquids with enhanced CO2 solubility using language models","authors":"Adroit T.N. Fajar , Guillaume Lambard , Md. Amirul Islam , Bidyut B. Saha , Zakiah D. Nurfajrin , Kevin Septioga","doi":"10.1016/j.aichem.2025.100089","DOIUrl":"10.1016/j.aichem.2025.100089","url":null,"abstract":"<div><div>This study presents a viable approach for designing eco-friendly ionic liquids (ILs) with enhanced CO<sub>2</sub> solubility using language models, specifically GPT-2 in conjunction with SMILES-X. The GPT-2 model was fine-tuned on a relatively small, unlabeled IL dataset and subsequently used to generate diverse IL structures. SMILES-X models, trained on IL datasets labeled with CO<sub>2</sub> solubility and eco-toxicity values, were employed to predict the properties of the generated ILs. Trends observed in the predicted IL properties were validated using density functional theory (DFT) and COSMO-RS calculations. The GPT-2 model was then fine-tuned iteratively, with the training data updated by including the top generated ILs from previous cycles. This iterative process led to a gradual improvement in the properties of the generated ILs. It was also observed, however, that continuously adding curated generated ILs to the training data eventually caused the model to produce correct but unrealistic IL structures. These findings highlight both the potential and limitations of language models in designing novel chemicals. Additionally, the CO<sub>2</sub> adsorption capacity of a surrogate IL was experimentally measured, demonstrating the potential of this approach in advancing decarbonization technologies.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 1","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “Machine learning assisted analysis and prediction of rubber formulation using existing databases” [Artif. Intell. Chem. 2/1 (2024) 100054] “使用现有数据库的机器学习辅助分析和预测橡胶配方”的勘误表[Artif。智能。化学。2/1 (2024)100054]

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2025-03-18 DOI: 10.1016/j.aichem.2025.100088

Wei Deng , Yuehua Zhao , Yafang Zheng , Yuan Yin , Yan Huan , Lijun Liu , Dapeng Wang

引用次数: 0

AI-driven prediction of drug activity against Toxoplasma gondii: Data augmentation and deep neural networks for limited datasets 人工智能驱动的弓形虫药物活性预测：有限数据集的数据增强和深度神经网络

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2025-02-05 DOI: 10.1016/j.aichem.2025.100084

Natalia V. Karimova , Ravithree D. Senanayake

{"title":"AI-driven prediction of drug activity against Toxoplasma gondii: Data augmentation and deep neural networks for limited datasets","authors":"Natalia V. Karimova , Ravithree D. Senanayake","doi":"10.1016/j.aichem.2025.100084","DOIUrl":"10.1016/j.aichem.2025.100084","url":null,"abstract":"<div><div>Toxoplasmosis, caused by <em>Toxoplasma gondii</em> (<em>T. gondii</em>), is a serious global health concern, particularly in immunocompromised individuals. Inhibiting the enzyme TgDHFR is a promising strategy for developing treatments. This Artificial Intelligence (AI)-driven Quantitative Structure-Activity Relationship (QSAR) study applies deep neural networks (DNNs) to predict pIC<sub>50</sub> values for potential inhibitors, using 2D and 3D molecular descriptors and fingerprints. To address training data limitations, we introduced a novel methodology combining targeted descriptor selection, Gaussian noise-based data augmentation, and an ensemble of DNNs. This approach significantly enhanced model performance, increasing the R² from 0.75 with the original dataset to 0.85. The model was further validated using two FDA-approved drugs for <em>T. gondii</em> treatment—pyrimethamine and trimethoprim—yielding relative errors of 3.35 % and 2.15 % in pIC<sub>50</sub> predictions compared to experimental values. Finally, the model was applied to screen FDA-approved drugs after filtering out molecules that did not align with the characteristics of the training dataset. The predicted pIC<sub>50</sub> values were further used to calculate ligand efficiency (LE), binding efficiency index (BEI), lipophilic ligand efficiency (LLE), and surface efficiency index (SEI), identifying the most promising TgDHFR inhibitors for further investigation. By leveraging AI and data augmentation approach, this study provides a powerful tool for pIC<sub>50</sub> predictions of TgDHFR inhibitors, which can be adapted to other systems.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 1","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven modelling of corrosion behaviour in coated porous transport layers for PEM water electrolyzers PEM水电解槽涂层多孔传输层腐蚀行为的数据驱动模型

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2025-02-25 DOI: 10.1016/j.aichem.2025.100086

Pramoth Varsan Madhavan , Leila Moradizadeh , Samaneh Shahgaldi , Xianguo Li

{"title":"Data-driven modelling of corrosion behaviour in coated porous transport layers for PEM water electrolyzers","authors":"Pramoth Varsan Madhavan , Leila Moradizadeh , Samaneh Shahgaldi , Xianguo Li","doi":"10.1016/j.aichem.2025.100086","DOIUrl":"10.1016/j.aichem.2025.100086","url":null,"abstract":"<div><div>Green hydrogen, produced through water electrolysis powered by renewable energy, is essential for a sustainable energy future. However, proton exchange membrane (PEM) water electrolyzers face durability issues, particularly corrosion of porous transport layers (PTLs), which limits their widespread commercialization. Protective coatings are used to mitigate PTL corrosion and improve durability. Traditional approaches to predicting coating performance in terms of corrosion resistance rely on extensive experimentation and intricate physical-electrochemical modelling, resulting in substantial time and cost. This study is the first to apply machine learning (ML) models to predict the corrosion behaviour of PTL coatings with varying alloy compositions for PEM water electrolyzers. Using Nb-Ta coated PTLs with different alloying ratios, coating performance is evaluated through potentiostatic polarization and end-of-life (EOL) tests. The data is split into two datasets: one for predicting corrosion current density and the other for predicting EOL voltage. Extreme gradient boosting (XGB) and artificial neural network (ANN) models are developed. To assess the models, mean absolute error (MAE) and mean squared error (MSE) are used as loss functions. The ANN model with the MSE loss function achieved the best performance, with an R<sup>2</sup> of 0.993 for corrosion current density. Additionally, the ANN model with a 0.1 dropout probability and MSE loss function resulted in an R<sup>2</sup> of 0.966 for EOL voltage predictions, outperforming the XGB models. These findings demonstrate the ability of ML models to accurately predict the anti-corrosion performance of PTL coatings, facilitating a faster approach to optimizing PTL coating compositions for PEM water electrolyzer applications.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 1","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small-dataset-orientated data-driven screening for catalytic propane activation 面向小数据集的催化丙烷活化数据驱动筛选

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2024-12-07 DOI: 10.1016/j.aichem.2024.100083

Jiaqi Chen , Junqing Li , Ziyi Liu, Shitao Sun, Shijia Zhou, Dongqi Wang

{"title":"Small-dataset-orientated data-driven screening for catalytic propane activation","authors":"Jiaqi Chen , Junqing Li , Ziyi Liu, Shitao Sun, Shijia Zhou, Dongqi Wang","doi":"10.1016/j.aichem.2024.100083","DOIUrl":"10.1016/j.aichem.2024.100083","url":null,"abstract":"<div><div>This work aims at the proper application of machine learning screening of direct propane dehydrogenation (PDH) reaction and oxidative dehydrogenation (ODH) of propane, which are two main protocols to convert propane to propylene and featured by limited available experimental data. Current studies mainly adopt trial-and-error strategy, which is time consuming and raises concerns on environment and health owing to the release of chemical waste. This motivates the introduction of data-driven research paradigm to alleviate the deficiency of the traditional trial-and-error strategy, which however relies on large quantity of high quality data. In this work, a dataset enveloping PDH and ODH data was constructed, and the performance of machine learning algorithms in the study of light alkane activation was evaluated, based on which a strategy appropriate for small dataset was proposed: for small unbalanced datasets, it is sensible to train the model by treating the dataset as a whole rather than to fuse multiple specific models based on divided smaller pieces of data. The results show that the trained models using ensemble algorithms exhibited the best predictability of propylene selectivity, i.e. CatBoost and random forest for PDH and LightGBM for ODH, respectively. Based on the optimal model, the key influencing factors in PDH and ODH were identified. This study demonstrates the proper use of data-driven strategy in the catalytic science, which can be adopted in other scientific problems that suffer from the limited available high quality data and contribute to the gain of novel understanding, e.g. the rational design and optimization of the catalytic systems.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 1","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation 利用带数据增强功能的元集合学习框架加强离子液体毒性预测

Artificial intelligence chemistry Pub Date : 2025-06-01 Epub Date: 2025-03-05 DOI: 10.1016/j.aichem.2025.100087

Safa Sadaghiyanfam , Hiqmet Kamberaj , Yalcin Isler

{"title":"Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation","authors":"Safa Sadaghiyanfam , Hiqmet Kamberaj , Yalcin Isler","doi":"10.1016/j.aichem.2025.100087","DOIUrl":"10.1016/j.aichem.2025.100087","url":null,"abstract":"<div><div>Ionic liquids are unique in their properties and potential to be green solvents. Still, the toxicity concern remains, compelling the need for excellent predictive models for safe design and application. This work reports the introduction of a general, robust meta-ensemble learning framework for predicting the toxicity of ionic liquids using molecular descriptors and fingerprints. The proposed model incorporates the Random Forest, Support Vector Regression, Categorical Boosting, Chemical Convolutional Neural Network as a base classifier and an Extreme Gradient Boosting meta-classifier. The framework uses Recursive Feature Elimination for feature selection and GridSearchCV for tuning the best hyperparameters. Without augmentation of the data, the RMSE equals 0.38, MAE equals 0.29, coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) equals 0.87, and Pearson correlation equals 0.94. Data augmentation further improved model performance: RMSE = 0.06, MAE = 0.024, <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.99, and a Pearson correlation of 0.99. In addition, this indicates that the data-augmented model outperforms all existing models with prominence in its strength and prediction capacity. Thus, the present framework provides a superior tool for computer-aided molecular design of safer and more effective ionic liquids.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 1","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence for drug repurposing against infectious diseases 人工智能为防治传染病重新设计药物用途

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-06-12 DOI: 10.1016/j.aichem.2024.100071

Anuradha Singh

{"title":"Artificial intelligence for drug repurposing against infectious diseases","authors":"Anuradha Singh","doi":"10.1016/j.aichem.2024.100071","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100071","url":null,"abstract":"<div><p>Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100071"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000290/pdfft?md5=061eb4d8766a2284afb22976a6d28b51&pid=1-s2.0-S2949747724000290-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0