Molecular Informatics最新文献

GDMol: Generative Double-Masking Self-Supervised Learning for Molecular Property Prediction. GDMol：用于分子特性预测的生成式双掩蔽自我监督学习。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-24 DOI: 10.1002/minf.202400146

Yingxu Liu, Qing Fan, Chengcheng Xu, Xiangzhen Ning, Yu Wang, Yang Liu, Yanmin Zhang, Yadong Chen, Haichun Liu

{"title":"GDMol: Generative Double-Masking Self-Supervised Learning for Molecular Property Prediction.","authors":"Yingxu Liu, Qing Fan, Chengcheng Xu, Xiangzhen Ning, Yu Wang, Yang Liu, Yanmin Zhang, Yadong Chen, Haichun Liu","doi":"10.1002/minf.202400146","DOIUrl":"https://doi.org/10.1002/minf.202400146","url":null,"abstract":"Background: Effective molecular feature representation is crucial for drug property prediction. Recent years have seen increased attention on graph neural networks (GNNs) that are pre-trained using self-supervised learning techniques, aiming to overcome the scarcity of labeled data in molecular property prediction. Traditional GNNs in self-supervised molecular property prediction typically perform a single masking operation on the nodes and edges of the input molecular graph, masking only local information and insufficient for thorough self-supervised training.Method: Hence, we propose a model for molecular property prediction based on generative double-masking self-supervised learning, termed as GDMol. This integrates generative learning into the self-supervised learning framework for latent representation, and applies a second round of masking to these latent representations, enabling the model to better capture global information and semantic knowledge of the molecules for a richer, more informative representation, thereby achieving more accurate and robust molecular property prediction.Results: Our experiments on 5 datasets demonstrated superior performance of GDMol in predicting molecular properties across different domains. Moreover, we used the masking operation to traverse through the gradient changes of each node, the magnitude and sign of which reflect the positive and negative contribution respectively of the local structure in the molecule to the prediction outcome. This in-depth interpretative analysis not only enhances the model's interpretability, but also provides more targeted insights and direction for optimizing drug molecules.Conclusions: In summary, this research offers novel insights on improving molecular property prediction tasks, and paves the way for further research on the application of generative learning and self-supervised learning in the field of chemistry.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142504416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction. GCLmf：基于硬阴性的新型分子图对比学习框架及其在毒性预测中的应用

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-18 DOI: 10.1002/minf.202400169

Xinxin Yu, Yuanting Chen, Long Chen, Weihua Li, Yuhao Wang, Yun Tang, Guixia Liu

{"title":"GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction.","authors":"Xinxin Yu, Yuanting Chen, Long Chen, Weihua Li, Yuhao Wang, Yun Tang, Guixia Liu","doi":"10.1002/minf.202400169","DOIUrl":"https://doi.org/10.1002/minf.202400169","url":null,"abstract":"In silico methods for prediction of chemical toxicity can decrease the cost and increase the efficiency in the early stage of drug discovery. However, due to low accessibility of sufficient and reliable toxicity data, constructing robust and accurate prediction models is challenging. Contrastive learning, a type of self-supervised learning, leverages large unlabeled data to obtain more expressive molecular representations, which can boost the prediction performance on downstream tasks. While molecular graph contrastive learning has gathered growing attentions, current models neglect the quality of negative data set. Here, we proposed a self-supervised pretraining deep learning framework named GCLmf. We first utilized molecular fragments that meet specific conditions as hard negative samples to boost the quality of the negative set and thus increase the difficulty of the proxy tasks during pre-training to learn informative representations. GCLmf has shown excellent predictive power on various molecular property benchmarks and demonstrates high performance in 33 toxicity tasks in comparison with multiple baselines. In addition, we further investigated the necessity of introducing hard negatives in model building and the impact of the proportion of hard negatives on the model.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction. ERL-ProLiGraph：用于结合亲和力预测的蛋白质配体图结构数据的增强表示学习。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-15 DOI: 10.1002/minf.202400044

Gloria Geine Paendong, Soualihou Ngnamsie Njimbouom, Candra Zonyfar, Jeong-Dong Kim

{"title":"ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.","authors":"Gloria Geine Paendong, Soualihou Ngnamsie Njimbouom, Candra Zonyfar, Jeong-Dong Kim","doi":"10.1002/minf.202400044","DOIUrl":"https://doi.org/10.1002/minf.202400044","url":null,"abstract":"Predicting Protein-Ligand Binding Affinity (PLBA) is pivotal in drug development, as accurate estimations of PLBA expedite the identification of promising drug candidates for specific targets, thereby accelerating the drug discovery process. Despite substantial advancements in PLBA prediction, developing an efficient and more accurate method remains non-trivial. Unlike previous computer-aid PLBA studies which primarily using ligand SMILES and protein sequences represented as strings, this research introduces a Deep Learning-based method, the Enhanced Representation Learning on Protein-Ligand Graph Structured data for Binding Affinity Prediction (ERL-ProLiGraph). The unique aspect of this method is the use of graph representations for both proteins and ligands, intending to learn structural information continued from both to enhance the accuracy of PLBA predictions. In these graphs, nodes represent atomic structures, while edges depict chemical bonds and spatial relationship. The proposed model, leveraging deep-learning algorithms, effectively learns to correlate these graphical representations with binding affinities. This graph-based representations approach enhances the model's ability to capture the complex molecular interactions critical in PLBA. This work represents a promising advancement in computational techniques for protein-ligand binding prediction, offering a potential path toward more efficient and accurate predictions in drug development. Comparative analysis indicates that the proposed ERL-ProLiGraph outperforms previous models, showcasing notable efficacy and providing a more suitable approach for accurate PLBA predictions.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pathway-based prediction of the therapeutic effects and mode of action of custom-made multiherbal medicines. 基于途径预测定制多草药的治疗效果和作用模式。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-15 DOI: 10.1002/minf.202400108

Akihiro Ezoe, Yuki Shimada, Ryusuke Sawada, Akihiro Douke, Tomokazu Shibata, Makoto Kadowaki, Yoshihiro Yamanishi

引用次数: 0

Review of the 8^th autumn school in chemoinformatics. 第八届化学信息学秋季学校回顾。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-15 DOI: 10.1002/minf.202400037

Johann Gasteiger

引用次数: 0

Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules. 腐蚀科学的化学信息学：数据驱动的有机分子腐蚀抑制模型。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-15 DOI: 10.1002/minf.202400082

Igor Baskin, Yair Ein-Eli

{"title":"Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.","authors":"Igor Baskin, Yair Ein-Eli","doi":"10.1002/minf.202400082","DOIUrl":"https://doi.org/10.1002/minf.202400082","url":null,"abstract":"This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure-property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data-driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

My 50 Years with Chemoinformatics. 我的化学信息学 50 年。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-15 DOI: 10.1002/minf.202400036

Johann Gasteiger

引用次数: 0

Navigating a 1E+60 Chemical Space of Peptide/Peptoid Oligomers. 浏览肽/肽低聚物的 1E+60 化学空间。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-10 DOI: 10.1002/minf.202400186

Markus Orsi, Jean-Louis Reymond

引用次数: 0

Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets. 工业规模的多任务 ADME/PK 预测：利用大型多样的实验数据集。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-01 Epub Date: 2024-07-08 DOI: 10.1002/minf.202400079

Moritz Walter, Jens M Borghardt, Lina Humbeck, Miha Skalic

{"title":"Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets.","authors":"Moritz Walter, Jens M Borghardt, Lina Humbeck, Miha Skalic","doi":"10.1002/minf.202400079","DOIUrl":"10.1002/minf.202400079","url":null,"abstract":"ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches. 从硅学方法看天然和合成 FFA4 激动剂的不同结合热点。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2024-10-01 Epub Date: 2024-07-24 DOI: 10.1002/minf.202400046

Guillaume Patient, Corentin Bedart, Naim A Khan, Nicolas Renault, Amaury Farce

{"title":"Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches.","authors":"Guillaume Patient, Corentin Bedart, Naim A Khan, Nicolas Renault, Amaury Farce","doi":"10.1002/minf.202400046","DOIUrl":"10.1002/minf.202400046","url":null,"abstract":"FFA4 has gained interest in recent years since its deorphanization in 2005 and the characterization of the Free Fatty Acids receptors family for their therapeutic potential in metabolic disorders. The expression of FFA4 (also known as GPR120) in numerous organs throughout the human body makes this receptor a highly potent target, particularly in fat sensing and diet preference. This offers an attractive approach to tackle obesity and related metabolic diseases. Recent cryo-EM structures of the receptor have provided valuable information for a potential active state although the previous studies of FFA4 presented diverging information. We performed molecular docking and molecular dynamics simulations of four agonist ligands, TUG-891, Linoleic acid, α-Linolenic acid, and Oleic acid, based on a homology model. Our simulations, which accumulated a total of 2 μs of simulation, highlighted two binding hotspots at Arg992.64 and Lys293 (ECL3). The results indicate that the residues are located in separate areas of the binding pocket and interact with various types of ligands, implying different potential active states of FFA4 and a highly adaptable binding intra-receptor pocket. This article proposes additional structural characteristics and mechanisms for agonist binding that complement the experimental structures.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141752164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0