Molecular Informatics最新文献

{"title":"The Chemical Space Spanned by Manually Curated Datasets of Natural and Synthetic Compounds with Activities against SARS-CoV-2.","authors":"Jude Y Betow, Gemma Turon, Clovis S Metuge, Simeon Akame, Vanessa A Shu, Oyere T Ebob, Miquel Duran-Frigola, Fidele Ntie-Kang","doi":"10.1002/minf.202400293","DOIUrl":"10.1002/minf.202400293","url":null,"abstract":"<p><p>Diseases caused by viruses are challenging to contain, as their outbreak and spread could be very sudden, compounded by rapid mutations, making the development of drugs and vaccines a continued endeavour that requires fast discovery and preparedness. Targeting viral infections with small molecules remains one of the treatment options to reduce transmission and the disease burden. A lesson learned from the recent coronavirus disease (COVID-19) is to collect ready-to-screen small molecule libraries in preparation for the next viral outbreak, and potentially find a clinical candidate before it becomes a pandemic. Public availability of diverse compound libraries, well annotated in terms of chemical structures and scaffolds, modes of action, and bioactivities are, therefore, crucial to ensure the participation of academic laboratories in these screening efforts, especially in resource-limited settings where synthesis, testing and computing capacity are scarce. Here, we demonstrate a low-resource approach to populate the chemical space of naturally occurring and synthetic small molecules that have shown in vitro and/or in vivo activities against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its target proteins. We have manually curated two datasets of small molecules (naturally occurring and synthetically derived) by reading and collecting (hand-curating) the published literature. Information from the literature reveals that a majority of the reported SARS-CoV-2 compounds act by inhibiting the main protease, while 25% of the compounds currently have no known target. Scaffold analysis and principal component analysis revealed that the most common scaffolds in the datasets are quite distinct. We then expanded the initially manually curated dataset of over 1200 compounds via an ultra-large scale 2D and 3D similarity search, obtaining an expanded collection of over 150 k purchasable compounds. The spanned chemical space significantly extends beyond that of a commercially available coronavirus library of more than 20 k small molecules and constitutes a good starting collection for virtual screening campaigns given its manageable size and proximity to hand-curated compounds.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400293"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693295","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}

{"title":"Structural and Dynamic Assessment of Disease-Causing Mutations for the Carnitine Transporter OCTN2.","authors":"Johannes Jokiel, Marcel Bermudez","doi":"10.1002/minf.202400002","DOIUrl":"https://doi.org/10.1002/minf.202400002","url":null,"abstract":"<p><p>Primary carnitine deficiency (PCD) is a rare autosomal recessive genetic disorder caused by missense mutations in the SLC22A5 gene encoding the organic carnitine transporter novel type 2 (OCTN2). This study investigates the structural consequences of PCD-causing mutations, focusing on the N32S variant. Using an alpha-fold model, molecular dynamics simulations reveal altered interactions and dynamics suggesting potential mechanistic changes in carnitine transport. In addition, we identify mutation hotspots (R169, E452) across the SLC family with the major facilitator superfamily (MFS) fold. Our data demonstrates the applicability of structural modeling for linking genetic information and clinical observations and providing a rationale for the influence of disease-causing mutations on protein dynamics.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 1","pages":"e202400002"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056046","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}

{"title":"Interpret Gaussian Process Models by Using Integrated Gradients.","authors":"Fan Zhang, Naoaki Ono, Shigehiko Kanaya","doi":"10.1002/minf.202400051","DOIUrl":"10.1002/minf.202400051","url":null,"abstract":"<p><p>Gaussian process regression (GPR) is a nonparametric probabilistic model capable of computing not only the predicted mean but also the predicted standard deviation, which represents the confidence level of predictions. It offers great flexibility as it can be non-linearized by designing the kernel function, made robust against outliers by altering the likelihood function, and extended to classification models. Recently, models combining deep learning with GPR, such as Deep Kernel Learning GPR, have been proposed and reported to achieve higher accuracy than GPR. However, due to its nonparametric nature, GPR is challenging to interpret. While Explainable AI (XAI) methods like LIME or kernel SHAP can interpret the predicted mean, interpreting the predicted standard deviation remains difficult. In this study, we propose a novel method to interpret the prediction of GPR by evaluating the importance of explanatory variables. We have incorporated the GPR model with the Integrated Gradients (IG) method to assess the contribution of each feature to the prediction. By evaluating the standard deviation of the posterior distribution, we show that the IG approach provides a detailed decomposition of the predictive uncertainty, attributing it to the uncertainty in individual feature contributions. This methodology not only highlights the variables that are most influential in the prediction but also provides insights into the reliability of the model by quantifying the uncertainty associated with each feature. Through this, we can obtain a deeper understanding of the model's behavior and foster trust in its predictions, especially in domains where interpretability is as crucial as accuracy.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400051"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716611","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}

{"title":"From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.","authors":"Alexey A Orlov, Tagir N Akhmetshin, Dragos Horvath, Gilles Marcou, Alexandre Varnek","doi":"10.1002/minf.202400265","DOIUrl":"10.1002/minf.202400265","url":null,"abstract":"<p><p>Dimensionality reduction is an important exploratory data analysis method that allows high-dimensional data to be represented in a human-interpretable lower-dimensional space. It is extensively applied in the analysis of chemical libraries, where chemical structure data - represented as high-dimensional feature vectors-are transformed into 2D or 3D chemical space maps. In this paper, commonly used dimensionality reduction techniques - Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) - are evaluated in terms of neighborhood preservation and visualization capability of sets of small molecules from the ChEMBL database.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400265"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780626","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}

{"title":"Extended Activity Cliffs-Driven Approaches on Data Splitting for the Study of Bioactivity Machine Learning Predictions.","authors":"Kenneth López-Pérez, Ramón Alain Miranda-Quintana","doi":"10.1002/minf.202400054","DOIUrl":"10.1002/minf.202400054","url":null,"abstract":"<p><p>The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its high data dependency, Machine Learning QSAR models will be directly influenced by the activity landscape. We propose several extended similarity and extended SALI methods to study the implications of ACs distribution on the training and test sets on the model's errors. Ununiform ACs and chemical space distribution tend to lead to worse models than the proposed uniform methods. ML modeling on AC-rich sets needs to be analyzed case-by-case. Proposed methods can be used as a tool to study the datasets, but as far as generalization, random splitting was the better-performing data splitting alternative overall.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400054"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668097","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}

{"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, Yu Xie, Yanmin Zhang, Yadong Chen, Haichun Liu","doi":"10.1002/minf.202400146","DOIUrl":"10.1002/minf.202400146","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400146"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","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}

{"title":"Protein Binding Site Representation in Latent Space.","authors":"Frederieke Lohmann, Stephan Allenspach, Kenneth Atz, Carl C G Schiebroek, Jan A Hiss, Gisbert Schneider","doi":"10.1002/minf.202400205","DOIUrl":"10.1002/minf.202400205","url":null,"abstract":"<p><p>Interpretability and reliability of deep learning models are important for computer-based drug discovery. Aiming to understand feature perception by such a model, we investigate a graph neural network for affinity prediction of protein-ligand complexes. We assess a latent representation of ligand binding sites and investigate underlying geometric structure in this latent space and its relation to protein function. We introduce an automated computational pipeline for dimensionality reduction, clustering, hypothesis testing, and visualization of latent space. The results indicate that the learned protein latent space is inherently structured and not randomly distributed. Several of the identified protein binding site clusters in latent space correspond to functional protein families. Ligand size was found to be a determinant of cluster geometry. The computational pipeline proved applicable to latent space analysis and interpretation and can be adapted to work for different datasets and deep learning models.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400205"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847041","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}

{"title":"Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model.","authors":"Yuto Shino, Hiromasa Kaneko","doi":"10.1002/minf.202400227","DOIUrl":"10.1002/minf.202400227","url":null,"abstract":"<p><p>Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties. However, this approach outputs many strings that do not follow the simplified molecular input line entry system grammar and generates unrealistic chemical structures in which the properties and activity do not satisfy the target values. In this study, we focus on hierarchical VAE using molecular graphs to address these issues. We confirm that the combination of hierarchical VAE and GMR does not generate invalid outputs and returns molecules that simultaneously satisfy multiple target values. Moreover, we use this method to identify several molecules that are predicted to exhibit activity against drug targets.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 1","pages":"e202400227"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142965748","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}

{"title":"Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst.","authors":"Philippe Gantzer, Ruben Staub, Yu Harabuchi, Satoshi Maeda, Alexandre Varnek","doi":"10.1002/minf.202400063","DOIUrl":"10.1002/minf.202400063","url":null,"abstract":"<p><p>Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography (\"chemography\") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 10⁵ structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400063"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910023","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}

{"title":"Structural Insight on the Selectivity of Calyx[4]Arene-Based Inhibitors of Mg^2+-Dependent Atp-Hydrolases.","authors":"Alexey Rayevsky, Maksym Platonov, Bulgakov Elijah, Dmytro Volochnyuk, Tetyana Veklich, Sergiy Cherenok, Roman Rodik, Vitaliy Kalchenko, Sergiy Kosterin","doi":"10.1002/minf.202400200","DOIUrl":"10.1002/minf.202400200","url":null,"abstract":"<p><p>Located in plasma membranes, ATP hydrolases are involved in several dynamic transport processes, helping to control the movement of ions across cell membranes. ATP hydrolase acts as a transport protein, converting energy from ATP hydrolysis into transport molecules against their concentration gradients. In addition to energy metabolism and active transport, ATP hydrolase is essential for maintaining cellular homeostasis and cell function. This study focused on the domain architecture model of P-type ATPases, which participate in the reaction cycles of ATP hydrolysis carried out by membrane transport systems - Na+, K+-ATPase and Ca2+, Mg2+-ATPase. Targeted modulation of Na+, K+-ATPase and Ca2+, Mg2+-ATPase by unnatural drugs is of greatest interest due to the lack of known effectors. This new discovery presents a convenient model based on our recent experimental studies of the membrane structures and myocytes of the uterine smooth muscle, the myometrium. This current study strongly supports the fact that nanosized calix[4]arenes functionalised on the upper rings of the macrocycle with biologically active phosphonic acid fragments can serve as selective and potent inhibitors of cation-transporting electroenzymes. This is how we discovered that calix[4]arene of methylenebisphosphonic acid C-97 and calix[4]arene of bis-aminophosphonic acid C-107 selectively and effectively (I0.5 <100 nM) inhibit the activity of Mg2+, ATP-dependent electrogenic Na+ K+ plasma membrane pump. As drug discovery in the field of Mg2+-ATPase inhibitors is uncharted territory, basic research holds the key to explaining and predicting the mechanism of interaction and action of different classes of compounds. In light of the presented results, new calix[4]arene compounds can be used as potent inhibitors of Mg2+, ATP-dependent electrogenic ion pumps.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202400200"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780628","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}