Molecular Informatics最新文献_第2页

Spherical GTM: A New Proposition for Visualization of Chemical Data. 球形GTM：化工数据可视化的新命题。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-06-01 DOI: 10.1002/minf.202500045

Farah Asgarkhanova, Marcou Gilles, Mikhail Volkov, Murielle Muzard, Richard Plantier-Royon, Caroline Rémond, Dragos Horvath, Alexandre Varnek

引用次数: 0

Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking-Based Screening Campaigns Using Publicly Available Docking Programs. 提高综合共识策略的可靠性，以促进基于对接的筛查活动，使用公开可用的对接计划。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-06-01 DOI: 10.1002/minf.2445

Valeria Scardino, M Justina Galarce, M Emilia Mignone, Claudio N Cavasotto

{"title":"Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking-Based Screening Campaigns Using Publicly Available Docking Programs.","authors":"Valeria Scardino, M Justina Galarce, M Emilia Mignone, Claudio N Cavasotto","doi":"10.1002/minf.2445","DOIUrl":"https://doi.org/10.1002/minf.2445","url":null,"abstract":"The use of docking-based virtual screening is today an established critical component within the drug discovery pipeline. In the context where the performance of molecular docking has been found to depend on the protein target and the program, consensus docking has been found to be a valuable approach to enhance the performance of high-throughput docking (HTD). We present and evaluate an integrated pose and ranking consensus approach that combines the advantages of pose consensus and the exponential consensus ranking (ECR) approach, using only publicly available docking programs (rDock, DOCK 6, Auto Dock 4, PLANTS, and Vina). Based on a thorough analysis performed to assess the optimal combination of matching poses and ECR thresholds, using a benchmarking set of 50 protein targets of diverse families and different property-matched ligand/decoy libraries, this enhanced pose/ranking consensus approach displayed a notably superior performance than the individual docking programs, and the ECR. This approach was also evaluated in HTD campaigns using larger libraries (∼1.1 million molecules) on six targets, thus obtaining an average improvement of the ECR of about 40%. We thus may say that this pose/ranking consensus methodology can be confidently used in prospective HTD campaigns using free-available docking programs.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 5-6","pages":"e2445"},"PeriodicalIF":2.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333535","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

Development of Machine Learning-Based Models for Mutagenicity Predictions with Applications to Non-Sugar Sweeteners. 基于机器学习的致突变性预测模型及其在非糖甜味剂中的应用。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-06-01 DOI: 10.1002/minf.202400357

Shilpayan Ghosh, Vinay Kumar, Kunal Roy

{"title":"Development of Machine Learning-Based Models for Mutagenicity Predictions with Applications to Non-Sugar Sweeteners.","authors":"Shilpayan Ghosh, Vinay Kumar, Kunal Roy","doi":"10.1002/minf.202400357","DOIUrl":"https://doi.org/10.1002/minf.202400357","url":null,"abstract":"Artificial sweeteners, often known as non-sugar sweeteners (NSSs), have been utilized as food additives since World War II. However, there is also concern regarding the mutagenicity potential of NSSs. Every new chemical registration in the food and pharmaceutical industries requires an evaluation of its mutagenic potential, which is essential for food safety. Most of the studies focus solely on determining the mutagenicity of NSSs through in vivo trials, which may be troublesome in terms of the time and cost required for experimental evaluation. To avoid the associated complexities concerning experimentation, a new approach methodology by developing machine learning (ML) models for mutagenicity predictions and selecting the best models by a stringent cross-validation analysis is explored. Two random splits (50/50) of a dataset of 6881 organic compounds for model development are used. Consensus predictions are provided for the mutagenic potential of an external set of 332 NSSs using six selected models (three best ML models based on cross-validation using either data splitting strategy) through voting and considering the applicability domain using two different approaches. In addition, to check the reliability of predictions, the model-derived consensus predictions have also been compared to the predictions generated by the k-nearest neighbor method using the virtual models for property evaluation of chemicals within a global architecture platform and the consensus method generated in the toxicity estimation software tool platform. Finally, based on the analysis, six compounds could be prioritized as mutagenic NSSs in this investigation. The developed models have been made available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/mutagenicity-predictor.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 5-6","pages":"e2400357"},"PeriodicalIF":2.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144302554","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

Deep Modeling of Gain-of-Function Mutations on Androgen Receptor. 雄激素受体功能获得性突变的深度建模。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-04-01 DOI: 10.1002/minf.202500018

Jiaying You, Jane Foo, Nada Lallous, Artem Cherkasov

引用次数: 0

Modeling Carbon Basicity. 碳碱度建模。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-03-01 DOI: 10.1002/minf.202400296

Robert Fraczkiewicz, Marvin Waldman

引用次数: 0

Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models. 神经系统疾病药物开发中的机器学习：血脑屏障渗透性预测模型综述。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-03-01 DOI: 10.1002/minf.202400325

Aryon Eckleel Nabi, Pedram Pouladvand, Litian Liu, Ning Hua, Cyrus Ayubcha

引用次数: 0

CoLiNN: A Tool for Fast Chemical Space Visualization of Combinatorial Libraries Without Enumeration. 一个不需要枚举的组合库的快速化学空间可视化工具。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-03-01 DOI: 10.1002/minf.202400263

Regina Pikalyova, Tagir Akhmetshin, Dragos Horvath, Alexandre Varnek

{"title":"CoLiNN: A Tool for Fast Chemical Space Visualization of Combinatorial Libraries Without Enumeration.","authors":"Regina Pikalyova, Tagir Akhmetshin, Dragos Horvath, Alexandre Varnek","doi":"10.1002/minf.202400263","DOIUrl":"10.1002/minf.202400263","url":null,"abstract":"Visualization of the combinatorial library chemical space provides a comprehensive overview of available compound classes, their diversity, and physicochemical property distribution - key factors in drug discovery. Typically, this visualization requires time- and resource-consuming compound enumeration, standardization, descriptor calculation, and dimensionality reduction. In this study, we present the Combinatorial Library Neural Network (CoLiNN) designed to predict the projection of compounds on a 2D chemical space map using only their building blocks and reaction information, thus eliminating the need for compound enumeration. Trained on 2.5 K virtual DNA-Encoded Libraries (DELs), CoLiNN demonstrated high predictive performance, accurately predicting the compound position on Generative Topographic Maps (GTMs). GTMs predicted by CoLiNN were found very similar to the maps built for enumerated structures. In the library comparison task, we compared the GTMs of DELs and the ChEMBL database. The similarity-based DELs/ChEMBL rankings obtained with \"true\" and CoLiNN predicted GTMs were consistent. Therefore, CoLiNN has the potential to become the go-to tool for combinatorial compound library design - it can explore the library design space more efficiently by skipping the compound enumeration.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 3","pages":"e202400263"},"PeriodicalIF":2.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11916640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Molecular Representation to Identify Isofunctional Molecules. 识别同功能分子的分子表征。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-03-01 DOI: 10.1002/minf.202400159

Philippe Pinel, Gwenn Guichaoua, Nicolas Devaux, Yann Gaston-Mathé, Brice Hoffmann, Véronique Stoven

{"title":"A Molecular Representation to Identify Isofunctional Molecules.","authors":"Philippe Pinel, Gwenn Guichaoua, Nicolas Devaux, Yann Gaston-Mathé, Brice Hoffmann, Véronique Stoven","doi":"10.1002/minf.202400159","DOIUrl":"10.1002/minf.202400159","url":null,"abstract":"The challenges of drug discovery from hit identification to clinical development sometimes involves addressing scaffold hopping issues, in order to optimise molecular biological activity or ADME properties, or mitigate toxicology concerns of a drug candidate. Docking is usually viewed as the method of choice for identification of isofunctional molecules, i. e. highly dissimilar molecules that share common binding modes with a protein target. However, the structure of the protein may not be suitable for docking because of a low resolution, or may even be unknown. This problem is frequently encountered in the case of membrane proteins, although they constitute an important category of the druggable proteome. In such cases, ligand-based approaches offer promise but are often inadequate to handle large-step scaffold hopping, because they usually rely on molecular structure. Therefore, we propose the Interaction Fingerprints Profile (IFPP), a molecular representation that captures molecules binding modes based on docking experiments against a panel of diverse high-quality proteins structures. Evaluation on the LH benchmark demonstrates the interest of IFPP for identification of isofunctional molecules. Nevertheless, computation of IFPPs is expensive, which limits its scalability for screening very large molecular libraries. We propose to overcome this limitation by leveraging Metric Learning approaches, allowing fast estimation of molecules IFPP similarities, thus providing an efficient pre-screening strategy that in applicable to very large molecular libraries. Overall, our results suggest that IFPP provides an interesting and complementary tool alongside existing methods, in order to address challenging scaffold hopping problems effectively in drug discovery.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 3","pages":"e202400159"},"PeriodicalIF":2.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657826","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

Molecular Odor Prediction Using Olfactory Receptor Information. 利用嗅觉受体信息进行分子气味预测。

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-03-01 DOI: 10.1002/minf.202400274

Yuta Wakutsu, Hiromasa Kaneko

引用次数: 0

Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values. Shapley值计算中不同方法生成的分子机器学习模型的解释比较

IF 2.8 4区医学

Molecular Informatics Pub Date : 2025-03-01 DOI: 10.1002/minf.202500067

Alec Lamens, Jürgen Bajorath

{"title":"Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values.","authors":"Alec Lamens, Jürgen Bajorath","doi":"10.1002/minf.202500067","DOIUrl":"10.1002/minf.202500067","url":null,"abstract":"Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determining individual predictions have frequently been identified in machine learning applications, the consistency of feature importance-based explanations of machine learning models using different attribution methods has not been thoroughly investigated. We have systematically compared model explanations in molecular machine learning. Therefore, a test system of highly accurate compound activity predictions for different targets using different machine learning methods was generated. For these predictions, explanations were computed using methodological variants of the Shapley value formalism, a popular feature attribution approach in machine learning adapted from game theory. Predictions of each model were assessed using a model-agnostic and model-specific Shapley value-based method. The resulting feature importance distributions were characterized and compared by a global statistical analysis using diverse measures. Unexpectedly, methodological variants for Shapley value calculations yielded distinct feature importance distributions for highly accurate predictions. There was only little agreement between alternative model explanations. Our findings suggest that feature importance-based explanations of machine learning predictions should include an assessment of consistency using alternative methods.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 3","pages":"e202500067"},"PeriodicalIF":2.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0