Artificial intelligence chemistry最新文献_第2页

Comparative study of machine learning methods for accurate prediction of logP and pKb logP和pKb准确预测的机器学习方法比较研究

Artificial intelligence chemistry Pub Date : 2026-06-01 Epub Date: 2025-12-01 DOI: 10.1016/j.aichem.2025.100101

Juda Baikété , Alhadji Malloum , Jeanet Conradie

{"title":"Comparative study of machine learning methods for accurate prediction of logP and pKb","authors":"Juda Baikété , Alhadji Malloum , Jeanet Conradie","doi":"10.1016/j.aichem.2025.100101","DOIUrl":"10.1016/j.aichem.2025.100101","url":null,"abstract":"<div><div>Machine learning (ML) has become a powerful tool for predicting molecular physicochemical properties. It finds applications in various research and development sectors, such as materials science, pharmaceutical chemistry, and environmental science. However, systematic comparisons between different types of properties remain limited. In this study, we developed two structured datasets: a logP dataset containing 1117 molecules and a pKb dataset containing 1268 molecules. For logP, each molecule is represented by 623 molecular descriptors generated exclusively by RDKit/Mordred, while a combination of 150 quantum chemistry descriptors from DFT calculations and molecular fingerprints derived from RDKit is used for pKb. Several ML algorithms were evaluated using an identical workflow, and the relevance of the descriptors was analyzed using SHAP, followed by feature pruning based on correlation. For the logP dataset, the LightGradBoost model achieved an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.94, an RMSE of 0.31, and an MAE of 0.42 on the independent test set, accurately reproducing experimental logP values in the range of −11.6 to 1.58. For pKb prediction, Random Forest (RF) proved most accurate, with an MAE of 1.69 and an RMSE of 1.68, with predicted values covering the entire range of experimental pKb values (−37 to 29.2). Our results indicate that, while RDKit/Mordred descriptors can predict logP with high accuracy, pKb remains a more challenging property to model, even when incorporating high-level DFT descriptors. The study therefore proposes a unified framework for the comparative evaluation of cross-property machine learning models and highlights the influence of the type of descriptor and the choice of algorithm on performance for chemically distinct properties.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651810","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

Electrostatic map for the hydration-induced frequency shifts of the NH stretching mode of peptide constructed using some machine learning models 利用一些机器学习模型构建了水合作用下肽的NH拉伸模式频移的静电图

Artificial intelligence chemistry Pub Date : 2025-12-01 Epub Date: 2025-11-14 DOI: 10.1016/j.aichem.2025.100098

Hajime Torii , Mikito Tsujimoto , Yukichi Kitamura

引用次数: 0

Machine learning-guided synthesis of prospective organic molecular materials: An algorithm with latent variables for understanding and predicting experimentally unobservable reactions 机器学习引导下的有机分子材料合成：一种用于理解和预测实验上不可观察反应的潜在变量算法

Artificial intelligence chemistry Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.aichem.2025.100096

Kazuhiro Takeda , Naoya Ohtsuka , Toshiyasu Suzuki , Norie Momiyama

{"title":"Machine learning-guided synthesis of prospective organic molecular materials: An algorithm with latent variables for understanding and predicting experimentally unobservable reactions","authors":"Kazuhiro Takeda , Naoya Ohtsuka , Toshiyasu Suzuki , Norie Momiyama","doi":"10.1016/j.aichem.2025.100096","DOIUrl":"10.1016/j.aichem.2025.100096","url":null,"abstract":"<div><div>Chemists have traditionally relied on heuristic approaches to qualitatively assess chemical structure–property relationships and interpret experimental outcomes. However, these methods are inherently limited in handling large volumes of data and integrating them effectively into experimental planning. Understanding the interrelationships among different substitution patterns of organic molecular materials is crucial for optimizing synthetic conditions and expanding their applicability. In this study, we developed a machine learning (ML) algorithm incorporating latent variables to predict unobservable reactions and synthetic conditions for organic materials, specifically perfluoro-iodinated naphthalene derivatives. The algorithm accurately estimated substitution pattern relationships and reaction yields, which were experimentally validated with high-yield outcomes. Our findings reveal that latent variables effectively capture underlying physicochemical relationships, achieving an R value > 0.99. This approach establishes an ML-guided framework that complements heuristic decision-making in chemistry and optimizes synthetic processes for the target molecule in an extrapolative manner. Further applications of this algorithm will focus on synthetic design and physicochemical property prediction, particularly for catalyst discovery and organic semiconductor optimization.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100096"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424603","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 : 2025-12-01 Epub 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":"2025-12-01","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

Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties 神经计算电路的神经网络：机器学习材料特性时对噪声和激活函数非均匀性的容忍度的计算研究

Artificial intelligence chemistry Pub Date : 2025-12-01 Epub Date: 2025-11-14 DOI: 10.1016/j.aichem.2025.100099

Ye min Thant , Methawee Nukunudompanich , Chu-Chen Chueh , Manabu Ihara , Sergei Manzhos

{"title":"Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties","authors":"Ye min Thant , Methawee Nukunudompanich , Chu-Chen Chueh , Manabu Ihara , Sergei Manzhos","doi":"10.1016/j.aichem.2025.100099","DOIUrl":"10.1016/j.aichem.2025.100099","url":null,"abstract":"<div><div>Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a digital computer is unwieldy (remote locations; small, mobile, and autonomous devices, extreme conditions, etc.). Neural networks (NN) implemented in such circuits, however, must contend with circuit noise and the non-uniform shapes of the neuron activation function (NAF) due to the dispersion of performance characteristics of circuit elements (such as transistors or diodes implementing the neurons). We present a computational study of the impact of circuit noise and NAF inhomogeneity in regression problems as a function of NN architecture and training regimes. We focus on one application that requires high-throughput ML: materials informatics, using as representative problem ML of formation energies vs. lowest-energy isomer of peri-condensed hydrocarbons, formation energies and band gaps of double perovskites, and zero point vibrational energies of molecules from QM9 dataset. We show that in these applications, NNs generally possess low noise tolerance with the model accuracy rapidly degrading with noise level. Single-hidden layer NNs, and NNs with larger-than-optimal sizes are somewhat more noise-tolerant. Models that show less overfitting (not necessarily the lowest test set error) are more noise-tolerant. Importantly, we demonstrate that the effect of activation function inhomogeneity can be palliated by retraining the NN using practically realized shapes of NAFs.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100099"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527993","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

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 : 2025-12-01 Epub 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":"2025-12-01","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

A method for predicting molecular point group based on graph neural networks 基于图神经网络的分子点群预测方法

Artificial intelligence chemistry Pub Date : 2025-12-01 Epub Date: 2025-11-04 DOI: 10.1016/j.aichem.2025.100097

Siyuan Zeng , Kuanping Gong , Yongquan Jiang , Yan Yang

{"title":"A method for predicting molecular point group based on graph neural networks","authors":"Siyuan Zeng , Kuanping Gong , Yongquan Jiang , Yan Yang","doi":"10.1016/j.aichem.2025.100097","DOIUrl":"10.1016/j.aichem.2025.100097","url":null,"abstract":"<div><div>Molecular symmetry is fundamental to understanding molecular properties, designing functional materials, and optimizing chemical structures. Traditional symmetry determination methods, based on mathematical and rule-based approaches, are often limited by high computational cost and low efficiency. At present, deep learning methods predicting molecular 3D conformations from 2D structures also neglect molecular symmetry and point group considerations. To address these challenges, we propose a novel task: predicting the point group of a molecule's most stable 3D conformation using only its 2D topological graph, thereby enabling symmetry-aware conformation prediction. We adopt Graph Neural Networks (GNNs) to learn from molecular graph structures, and evaluate several GNN variants on this task. Among them, the Graph Isomorphism Network (GIN) achieves the highest accuracy by effectively capturing both local connectivity and global structural information. Experiments on the QM9 dataset show that our method achieves 92.7 % accuracy and an F1-score of 0.924 on the test set, significantly surpassing both traditional approaches and other GNN-based methods. This work demonstrates the potential of deep learning in automated, efficient, and accurate molecular symmetry prediction, providing a valuable tool for future research in computational chemistry and material science.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"3 2","pages":"Article 100097"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473664","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 : 2025-12-01 Epub 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":"2025-12-01","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

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 : 2025-12-01 Epub 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":"2025-12-01","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 (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 : 2025-12-01 Epub 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":"2025-12-01","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