Artificial intelligence chemistry最新文献

Optimization of catalyst composition and performance for PEM fuel cells: A data-driven approach PEM燃料电池催化剂组成和性能的优化：数据驱动的方法

Artificial intelligence chemistry Pub Date : 2025-09-14 DOI: 10.1016/j.aichem.2025.100095

Pramoth Varsan Madhavan , Xin Zeng , Samaneh Shahgaldi , Sushanta K. Mitra , Xianguo Li

{"title":"Optimization of catalyst composition and performance for PEM fuel cells: A data-driven approach","authors":"Pramoth Varsan Madhavan , Xin Zeng , Samaneh Shahgaldi , Sushanta K. Mitra , Xianguo Li","doi":"10.1016/j.aichem.2025.100095","DOIUrl":"10.1016/j.aichem.2025.100095","url":null,"abstract":"<div><div>Transportation’s rising negative environmental impacts and energy demands highlight the urgent need for clean alternative power sources such as proton exchange membrane (PEM) fuel cells. However, the high cost of platinum catalysts hinders its commercialization, making the development of low-platinum, high-performance catalysts essential for achieving net-zero targets. This study employs a data-driven machine learning approach to optimize the oxygen reduction reaction (ORR) catalyst composition and predict its long-term performance using extreme gradient boosting (XGB), artificial neural networks (ANN), and genetic algorithm (GA). Linear sweep voltammetry (LSV) data is collected for three distinct catalyst compositions and divided into separate datasets. Data is preprocessed and model hyperparameters are fine-tuned to enhance model accuracy. XGB models trained on these datasets accurately predicted LSV polarization plots for unseen data, as evidenced by R<sup>2</sup> values > 0.99. To further optimize ORR catalyst design, an ANN model trained on data from three different catalyst compositions is integrated with a genetic algorithm. This predictive framework effectively identified optimal catalyst composition by maximizing the mass activity of the catalyst. Experimental validation of this optimized composition yielded strong agreement with predicted LSV current values, confirming the reliability of the ANN-GA approach. This research underscores the potential of machine learning-based predictive frameworks to accelerate the development of advanced ORR catalysts for PEM fuel cells.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104422","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

GraphSLA: Graph machine learning for predicting small molecule - lncRNA associations GraphSLA：用于预测小分子- lncRNA关联的图机器学习

Artificial intelligence chemistry Pub Date : 2025-08-11 DOI: 10.1016/j.aichem.2025.100094

Ashish Panghalia, Parth Kumar, Vikram Singh

{"title":"GraphSLA: Graph machine learning for predicting small molecule - lncRNA associations","authors":"Ashish Panghalia, Parth Kumar, Vikram Singh","doi":"10.1016/j.aichem.2025.100094","DOIUrl":"10.1016/j.aichem.2025.100094","url":null,"abstract":"<div><div>Long non-coding RNAs are increasingly reported to have critical roles in gene expression, regulation of cellular processes, and in the onset and manifestation of various diseases. Recent studies have highlighted the role of small molecules (SMs) in controlling the functioning of lncRNAs, making SM-lncRNA associations (SLAs) a promising approach for therapeutic development. In this study, using 3563 curated SLAs among 115 SMs and 2826 lncRNAs, five graph learning algorithms are developed for the SLA classification. Node2Vec was used to extract the contextual features of SMs and lncRNAs from their bipartite association network, while Mol2Vec and Doc2Vec algorithms were used for the extraction of molecular features of the SMs and lncRNAs, respectively. Principal components corresponding to the 95 % variability in feature vectors were used to train five graph-learning models, namely, Graph Neural Network (GNN), Graph Convolutional Network (GCN), Graph Attention Network (GAT), Graph Sample and Aggregate (GraphSAGE), and Simplified Graph Convolution (SGConv). Among these five models, GraphSAGE achieved the best performance with an accuracy of 98.0 % and an AUC-ROC of 99.4 % when evaluated over 10 training epochs. Generalizability studies were also conducted to assess whether the developed models maintain robustness, reliability, and practical utility when applied to real-world data. The overall results reported in this work exhibit better performance over previously developed SLA prediction methods. This study underscores the potential of graph-learning methods to effectively capture the intricate associations among SMs and lncRNAs, facilitating the discovery of novel SLAs.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841823","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

Machine learning prediction of pKa of organic acids 有机酸pKa的机器学习预测

Artificial intelligence chemistry Pub Date : 2025-08-08 DOI: 10.1016/j.aichem.2025.100092

Juda Baikété , Alhadji Malloum , Jeanet Conradie

{"title":"Machine learning prediction of pKa of organic acids","authors":"Juda Baikété , Alhadji Malloum , Jeanet Conradie","doi":"10.1016/j.aichem.2025.100092","DOIUrl":"10.1016/j.aichem.2025.100092","url":null,"abstract":"<div><div>The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to cross the plasma membrane. It affects the chemical properties of absorption, distribution, metabolism, excretion, and toxicity. Thus, accurate prediction of pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. Here, we present four tree-based machine learning models for pKa prediction of organic molecules. The four models, Random Forest (RF), Extra Trees (ExTr), Histogram Gradient Boosting (HGBoost), and Gradient Boosting (GBoost), were trained on an experimental pKa dataset and tested on SAMPL6 and SAMPL7, two external datasets. Structural and organic parameter (SPOC)-based descriptors were introduced to represent the physicochemical properties of molecules. Further molecular descriptors have been generated using density functional theory (DFT) calculations, and RDKit library. The model trained with the ExTr algorithm showed the best prediction performance with an overall mean absolute error (MAE) value of 1.41 pKa units. Our model (ExTr) outperforms selected models on a range of benchmark data, while offering two unique advantages: (1) full transparency (open descriptors and data) in contrast to proprietary black boxes, and (2) reduced computational cost compared to hybrid QM/ML approaches. While specialized tools like QupKake (MAE <span><math><mo>=</mo></math></span> 0.67) achieve better accuracy, our framework provides an open-source basis for interpretable pKa predictions, efficiently combining molecular physics and machine learning. This model represents a significant advancement in pKa prediction, offering a powerful tool for various applications in chemistry and beyond.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830953","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

Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) modeling of the tissue-to-plasma partition coefficient (Kp) of drugs across different tissues 机器学习（ML）驱动的药物在不同组织间的组织-血浆分配系数（Kp）的定量结构-药代动力学关系（QSPKR）建模

Artificial intelligence chemistry Pub Date : 2025-07-31 DOI: 10.1016/j.aichem.2025.100093

Souvik Pore, Kunal Roy

{"title":"Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) modeling of the tissue-to-plasma partition coefficient (Kp) of drugs across different tissues","authors":"Souvik Pore, Kunal Roy","doi":"10.1016/j.aichem.2025.100093","DOIUrl":"10.1016/j.aichem.2025.100093","url":null,"abstract":"<div><div>In drug discovery, estimating the drug candidate's pharmacokinetic (PK) parameters is crucial for determining its safety and efficacy within the body. The tissue-to-plasma partition coefficient (Kp) indicates how a drug partitions within a tissue, potentially leading to tissue-specific activity or toxicity. Therefore, determining K<sub>p</sub> values for a drug is essential for its safety assessment. However, only a limited number of such studies are available. Here, we developed machine learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) models to predict the K<sub>p</sub> values for drugs across 11 different tissues. Initially, we developed models to predict K<sub>p</sub> values for drugs with missing K<sub>p</sub> values for specific tissues within the dataset solely based on the structural and physicochemical properties of the drugs. Subsequently, another set of models was developed using both structural and physicochemical properties and the K<sub>p</sub> values from other tissues. In this case, predicted values from the initial models were also incorporated where experimental K<sub>p</sub> values were unavailable. These models demonstrate significant improvement in predictability (Q<sup>2</sup><sub>F1</sub> = 0.79–0.95, Q<sup>2</sup><sub>F2</sub> = 0.78–0.95) for a drug compared to the initial models. Here, we conducted a screening using a true external dataset from the SIDER database. This analysis indicates that compounds with higher tissue partitioning are more likely to exhibit toxicity to that specific tissue. Finally, a Python-based tool (<span><span>https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/kp-calculator</span><svg><path></path></svg></span>) was created to predict K<sub>p</sub> values for drugs in different tissues.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763649","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

ChiralCat: Molecular chirality classification with enhanced spatial representation using learnable queries ChiralCat：使用可学习查询增强空间表示的分子手性分类

Artificial intelligence chemistry Pub Date : 2025-06-27 DOI: 10.1016/j.aichem.2025.100091

Yichuan Peng , Gufeng Yu , Runhan Shi , Letian Chen , Xi Wang , Wenjie Du , Xiaohong Huo , Yang Yang

{"title":"ChiralCat: Molecular chirality classification with enhanced spatial representation using learnable queries","authors":"Yichuan Peng , Gufeng Yu , Runhan Shi , Letian Chen , Xi Wang , Wenjie Du , Xiaohong Huo , Yang Yang","doi":"10.1016/j.aichem.2025.100091","DOIUrl":"10.1016/j.aichem.2025.100091","url":null,"abstract":"<div><div>Molecular chirality is a key focus of research in chemistry and biology. In nature, there are many complex categories of chirality and it can strongly alter biochemical activities and interactions, particularly in asymmetric catalysis and protein–drug binding. Despite advancements in molecular property prediction approaches, a computational method capable of identifying chiral types has been absent, impeding progress in chirality studies. This gap is primarily due to the inability of current molecular representation models to capture chiral-related spatial features and the scarcity of annotated datasets for complex chiral types. To address these limitations, we develop ChiralCat, a pioneering machine learning method for molecular chirality classification. ChiralCat’s core is a pre-trained multi-modal classifier that enhances spatial molecular representations. This is achieved through learnable queries, guided by chirality-related descriptions generated by a large language model (LLM). To facilitate the model’s training, we construct an extensive chiral molecule dataset comprising 17,181 molecules across various chiral categories. Our experimental results, both quantitative and visualized, reveal that ChiralCat outperforms existing 3D molecular representation learning models in capturing spatial information pertinent to chirality, thereby exhibiting superior capability in discerning complex chiral types.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548861","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

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 DOI: 10.1016/j.aichem.2024.100081

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 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

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 : 2025-05-22 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":"2025-05-22","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

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 : 2025-03-05 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":"2025-03-05","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

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 : 2025-03-01 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":"2025-03-01","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