{"title":"Ligand B-Factor Index: A Metric for Prioritizing Protein-Ligand Complexes in Docking.","authors":"Liliana Halip, Cristian Neanu, Sorin Avram","doi":"10.1002/minf.70010","DOIUrl":"10.1002/minf.70010","url":null,"abstract":"<p><p>Docking is a structure-based cheminformatics tool broadly employed in early drug discovery. Based on the tridimensional structure of the protein target, docking is used to predict the binding interactions between the protein and a ligand, estimate the corresponding binding affinity, or perform virtual screenings (VSs) to identify new active compounds. This study introduces the ligand B-factor index (LBI), a novel computational metric for prioritizing protein-ligand complexes for docking. Unlike other metrics, LBI directly compares atomic displacements in the ligand and binding site. LBI is defined as the ratio of the median atomic B-factor of the binding site to that of the bound ligand. Using the comparative assessment of scoring functions (CASF-2016) dataset, we evaluated the effectiveness of LBI in guiding the selection of protein-ligand complexes to enhance docking performance. Our results show a moderate correlation (Spearman ρ ~ 0.48) between LBI and the experimental binding affinities, outperforming several docking scoring functions. Additionally, LBI correlates with improved redocking success (root mean square deviation < 2 Å), underlying the significance of a ligand-focused metric. While LBI outperforms other metrics such as the protein B-factor index and resolution, its utility in VS docking remains to be further investigated. LBI is easy to compute, interpretable, applicable in structure-based cheminformatics, and freely available for calculation at https://chembioinf.ro/tool-bi-computing.html.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 9","pages":"e202500127"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145033654","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}
Omer Kaspi, Yaniv Y Avissar, Arnon Grafit, Ron Chibel, Olga Girshevitz, Hanoch Senderowitz
{"title":"Machine Learning-Based Identification of Petroleum Distillates and Gasoline Traces Using Measured and Synthetic GC Spectra from Collected Samples.","authors":"Omer Kaspi, Yaniv Y Avissar, Arnon Grafit, Ron Chibel, Olga Girshevitz, Hanoch Senderowitz","doi":"10.1002/minf.70008","DOIUrl":"https://doi.org/10.1002/minf.70008","url":null,"abstract":"<p><p>Ignition cases involving arsons are typically handled by forensic experts who examine spectra of samples collected from scenes of fire to test for the existence or absence of ignitable liquids. This is tedious work, since many cases do not involve such liquids. To facilitate this process, we have developed in this work a Machine Learning (ML)-based workflow for samples' classification based on their gas chromatography (GC) chromatograms (i.e., spectra). To this end, annotated spectra of 181 samples containing three groups of liquids (petroleum distillates, gasoline, and an assortment of other substances) collected from fire scenes as well as two reference databases were obtained from the Israeli Department of Identification and Forensic Sciences (DIFS). These spectra were used for the derivation of ML-based classification models using three algorithms, namely, kNN, representative spectrum, and random forest (RF) giving rise to reliable predictions. To increase the size of the dataset to a level that would enable the usage of more advanced ML algorithms, we have used the experimental spectra to develop a new spectra synthesis algorithm and utilized it to generate a large dataset of synthetic spectra. This dataset was used for the derivation of new kNN, RF, and representative spectrum models as well as deep learning (DL) models producing F1-scores over an independent test set composed entirely of \"real\" spectra ranging from 0.74-0.95, 0.86-0.95, 0.30-0.75, and 0.85-0.96 for kNN, RF, representative spectrum, and DL, respectively. Following the completion of the work, a second set of real spectra was provided to us by DIFS, and modeling it with the second set of models yielded F1-scores ranging from 0.92-0.96, 0.96-1.00, 0.71-0.82, and 0.95-0.98 for kNN, RF, representative spectrum, and DL, respectively. These results therefore suggest that for this dataset, performances depend more on the size of the dataset used for model training than on the ML algorithm. We propose that the workflow and spectra synthesis algorithm developed in this work could be readily applied to other forensic domains where samples are characterized by spectra, either solely or in combination with other parameters.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 8","pages":"e202400371"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12371388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144961933","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":"Integrating Generative Pretrained Transformer and Genetic Algorithms for Efficient and Diverse Molecular Generation.","authors":"Chengcheng Xu, Chen Zeng, Xi Yang, Yingxu Liu, Xiangzhen Ning, Lidan Zheng, Yang Liu, Qing Fan, Chao Xu, Haichun Liu, Xian Wei, Yadong Chen, Yanmin Zhang, Rui Gu","doi":"10.1002/minf.70005","DOIUrl":"https://doi.org/10.1002/minf.70005","url":null,"abstract":"<p><p>In computer-aided drug design, molecular generation models play a crucial role in accelerating the drug development process. Current models mainly fall into two categories: deep learning models with high performance but poor interpretability and heuristic algorithms with better interpretability but limited performance. In this study, we introduce an innovative molecular generation model, the compound construction model (CCMol), which integrates the powerful generative capabilities of the generative pretrained transformer (GPT) and the efficient optimization mechanisms of genetic algorithms (GA) to achieve effective and innovative molecular structures. Specifically, our approach uses structure-based drug design comprising both ligand and protein primary structure-based aspects. CCMol integrates GPT for initial molecular generation and GA for iterative optimization of physicochemical and biological properties. The model's reliability was validated by generating molecules targeting three critical disease-related proteins (GLP1, WRN, and JAK2). The results indicate that CCMol is on average with current advanced models in multiple indicators and performs better than the baseline model in terms of structure diversity and drug-related properties indicators, demonstrating that CCMol exhibits outstanding performance in developing novel and effective candidate drug molecules, particularly suitable for expanding the chemical validity of candidate structures at the early stages of drug discovery.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 8","pages":"e202500094"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784859","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}
Esteban Bertsch-Aguilar, Antonio Piedra, Daniel Acuña, Sebastián Suñer, Sylvana Pinheiro, William J Zamora
{"title":"LiProS: Findable, Accessible, Interoperable, and Reusable Data Simulation Workflow to Predict Accurate Lipophilicity Profiles for Small Molecules.","authors":"Esteban Bertsch-Aguilar, Antonio Piedra, Daniel Acuña, Sebastián Suñer, Sylvana Pinheiro, William J Zamora","doi":"10.1002/minf.70007","DOIUrl":"https://doi.org/10.1002/minf.70007","url":null,"abstract":"<p><p>Lipophilicity is a fundamental physicochemical property widely used to evaluate key parameters in drug design, materials science, and food engineering. It plays a critical role in predicting membrane permeability, absorption, and distribution of compounds. Moreover, lipophilicity is commonly integrated into scoring functions to model biomolecular interactions and serves as an important molecular descriptor in machine learning models for property prediction and compound classification. The election of the appropriate pH-dependent lipophilicity ( <math> <semantics><mrow><mi>log</mi> <msub><mi>D</mi> <mrow><mtext>pH</mtext></mrow> </msub> </mrow> <annotation>$$ mathrm{log} {D}_{pH} $$</annotation></semantics> </math> ) model is important to ensure its accuracy. The incorporation of the ion apparent partition coefficient ( <math> <semantics> <mrow><msubsup><mi>P</mi> <mi>I</mi> <mtext>app</mtext></msubsup> </mrow> <annotation>$$ {P}_{text{I}}^{text{app}}$$</annotation></semantics> </math> ) into predictions of pH-dependent lipophilicity profiles can be essential for accurately reproducing experimental results. In accordance with the principles for findable, accessible, interoperable, and reusable data to improve data management and sharing, here, we introduce LiProS, a FAIR workflow that is easily accessible through a Google Colab notebook. LiProS assists researchers in efficiently determining the appropriate pH-dependent lipophilicity profile based on the SMILES code of their molecules of interest. In addition, LiProS demonstrated its utility in the analysis of ionizable compounds within the NAPRORE-CR natural products database, enabling the identification of the most appropriate lipophilicity formalism tailored to the physicochemical characteristics of these compounds.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 8","pages":"e202500136"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962005","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 Flexibility and Shape Similarity Contribute to Exclusive Functions of Certain Atg8 Isoforms in the Autophagy Process.","authors":"Alexey Rayevsky, Eliah Bulgakov, Mariia Stykhylias, Sergey Ozheredov, Svetlana Spivak, Yaroslav Blume","doi":"10.1002/minf.70004","DOIUrl":"https://doi.org/10.1002/minf.70004","url":null,"abstract":"<p><p>Despite the abundance of systematically collected experimental data and facts, the multistep process of autophagy still contains many dark spots. One concerns the background selectivity of interactions between certain autophagy-related protein (ATG8) isoforms and their receptors/adaptors in plants during the autophagy process. By regulating phagophore initiation, expansion, and maturation, these proteins control the assembly of numerous autophagy proteins at this key docking platform. Bioinformatics analysis of human, yeast, and plant ATG8 amino acid sequences allow us to build a sequence tree of plant ATG8s, divided in three groups. We perform a structural study aimed at revealing some of the underlying reasons for the differences in the selectivity of ATG8 isoforms. A series of molecular dynamics (MD) simulations are performed to explain the stage-dependent functionality of ATG8. The conserved secondary structure and folding across all ATG8 proteins, resulting in nearly identical protein-protein interaction interfaces, makes this study particularly important and interesting. Recognizing the dual role of the LC3 interacting region (LIR) in autophagosome biogenesis and recruitment of the anchored selective autophagy receptor (SAR), we perform a mobility domain analysis. To this end, the amino acid sequence associated with the LIR docking site (LDS) interface is localized and subjected to root mean square deviation (RMSD)-based clustering analysis. Starting from Atg8-targeted protein-peptide docking, we attempt to identify conformational changes in the contact region of the corresponding adaptors and receptors involved in the common biogenesis events in autophagy. For the molecular dynamics, we select three representatives, sharing common patterns with other members of the groups. The resulting ATG8-peptide complexes display a significant preference for binding specific partners by different ATG8 isotypes.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 7","pages":"e202500025"},"PeriodicalIF":2.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659700","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}
Emma Svensson, Emma Granqvist, Tomas Bastys, Christos Kannas, Mikhail Kabeshov, Samuel Genheden, Ola Engkvist, Thierry Kogej
{"title":"Network Analysis of the Organic Chemistry in Patents, Literature, and Pharmaceutical Industry.","authors":"Emma Svensson, Emma Granqvist, Tomas Bastys, Christos Kannas, Mikhail Kabeshov, Samuel Genheden, Ola Engkvist, Thierry Kogej","doi":"10.1002/minf.202500011","DOIUrl":"10.1002/minf.202500011","url":null,"abstract":"<p><p>Chemical reactions can be connected in large networks such as knowledge graphs. In this way, prior work has been able to draw meaningful conclusions about the properties and structures involved in organic chemistry reactions. However, the research has focused on public sources of organic synthesis that might lack the intricate details of the synthetic routes used in in-house drug discovery. In this work, previous analyses are expanded to also include an in-house electronic lab notebook (ELN) source, such that we can compare it to knowledge graphs that were constructed from US Patent and Trademark Office (USPTO) and Reaxys. We found that the Reaxys knowledge graph is the most interconnected and has the largest proportion of nodes belonging to the core, whereas the USPTO is much less connected and only has a small core. The ELN knowledge graph falls between these extremes in connectivity and it does not have any core. The hub molecules of ELN and USPTO are most similar, primarily represented by small, organic building blocks. We hypothesize that these differences can be attributed to the different origins of the data in the three sources. We discuss what impact this might have on synthesis prediction modelling.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 7","pages":"e202500011"},"PeriodicalIF":2.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659699","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}
Anton Cherednichenko, Sergii Afonin, Oleg Babii, Taras Voitsitskyi, Roman Stratiichuk, Ihor Koleiev, Volodymyr Vozniak, Nazar Shevchuk, Zakhar Ostrovsky, Semen Yesylevskyy, Alan Nafiiev, Serhii Starosyla, Anne S Ulrich, Aigars Jirgensons, Igor V Komarov
{"title":"Neural Network Models for Prediction of Biological Activity using Molecular Dynamics Data: A Case of Photoswitchable Peptides.","authors":"Anton Cherednichenko, Sergii Afonin, Oleg Babii, Taras Voitsitskyi, Roman Stratiichuk, Ihor Koleiev, Volodymyr Vozniak, Nazar Shevchuk, Zakhar Ostrovsky, Semen Yesylevskyy, Alan Nafiiev, Serhii Starosyla, Anne S Ulrich, Aigars Jirgensons, Igor V Komarov","doi":"10.1002/minf.70001","DOIUrl":"10.1002/minf.70001","url":null,"abstract":"<p><p>Prediction of biological activities of chemical compounds by the machine learning techniques in general and the neural networks (NNs) in particular, is usually based on the analysis of their binding to the target of interest. If such affinity data is not available, the ligand-based approaches can be used where the NN models are trained to assess similarity of compounds to those with known biological activity. Obviously, this approach only works well if the similarity between the training set and the evaluated molecules is sufficiently high. In the case of large and conformationally flexible organic compounds, the activity becomes dependent not only on chemical identity but also on the dynamics of molecular motions, which imposes significant challenges to existing approaches based on static structural 2D and 3D molecular descriptors. A prominent example of compounds, which are especially challenging for existing NN activity prediction techniques, are photoswitchable macrocyclic peptides containing a diarylethene \"photoswitch\" (DAE). These molecules exist in two isomeric forms with remarkably different biological activities, which are interconvertible by light of different wavelengths. Activity prediction models have to distinguish in this case not only between the different peptides but also between the photoisomers of the same peptide. In this work, we demonstrate that the features extracted from classical molecular dynamics (MD) trajectories are superior to conventional 2D or 3D descriptor-based features when used in activity prediction NN models of DAE-containing photoswitchable peptides. Using MD-derived features, we successfully created two NN models that predict activities of photoswitchable peptidomimetics, analogs of the natural peptidic antibiotic gramicidin S. The first model precisely predicts the cytotoxic activity of similar peptide analogs. The second model reliably predicts the differences in the biological activities of DAE photoisomers of the same peptide, even if the type of its activity differs from one in the training dataset. Our results demonstrate that accounting for MD-derived dynamic features allows generalizing the ligand-based activity prediction NN models to the cases of large and conformationally flexible molecules, which were previously considered intractable by this class of models.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 7","pages":"e70001"},"PeriodicalIF":2.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12257427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144626740","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":"Rapid Assessment of Virtually Synthesizable Chemical Structures via Support Vector Machine Models.","authors":"Yuto Iwasaki, Tomoyuki Miyao","doi":"10.1002/minf.70000","DOIUrl":"10.1002/minf.70000","url":null,"abstract":"<p><p>Support vector machine (SVM) and support vector regression (SVR) are widely used for building quantitative structure-activity relationship models for small- and medium-sized datasets. Although SVM and SVR models can efficiently predict compound activity, evaluating billions of molecules remains challenging, which sometimes occurs when screening the virtual molecules derived through virtual synthesis. Herein, we present an SVM-/SVR-based method for screening virtually synthesizable molecules based on their reactants. The proposed method employs a combination of reactant-wise kernel functions for fast evaluation without sacrificing prediction accuracy. Tested on 120 small molecular activity datasets against 10 macromolecule targets, the proposed SVR models with data augmentation worked equally to standard SVR models with the Tanimoto kernel. As a demonstration, exhaustive 6.4 × 10<sup>12</sup> reactant combinations were evaluated by an SVR model within 8 days on a single desktop computer, enabling large-scale screening without sampling.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 7","pages":"e202500039"},"PeriodicalIF":2.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675311","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}
Vikas Yadav, Mohammad Kashif, Zenab Kamali, Samudrala Gourinath, Naidu Subbarao
{"title":"In Silico Identification of Novel and Potent Inhibitors Against Mutant BRAF (V600E), MD Simulations, Free Energy Calculations, and Experimental Determination of Binding Affinity.","authors":"Vikas Yadav, Mohammad Kashif, Zenab Kamali, Samudrala Gourinath, Naidu Subbarao","doi":"10.1002/minf.202400372","DOIUrl":"https://doi.org/10.1002/minf.202400372","url":null,"abstract":"<p><p>BRAF is a proto oncogene that functions as a key signal transducer in the MAPK-ERK pathway, which regulates cell growth, division, and survival. Mutations in BRAF, particularly the V600E substitution in its kinase domain, are major drivers in melanoma and several other metastatic cancers, including breast, colorectal, NSCLC, and gastrointestinal cancers. In this study, novel inhibitors targeting the BRAF(V600E) mutant using a structure-based drug design approach are identified. Four chemical libraries ChemDiv Kinase, ChemDiv Anticancer, NCI, and ChEMBL Kinase SARfari are screened. Compounds from the ChemDiv Anticancer database show better Glide scores comparable to the FDA-approved BRAF inhibitor Vemurafenib. The compounds P184-1419 and P184-1479 score -12.688 and -12.012 kcal/mol, respectively, versus -14.288 kcal/mol for Vemurafenib. Top hits are further validated using GOLD docking, X-score ranking, and interaction profiling via LigPlot. Molecular dynamics simulations, principal component analysis, and free energy calculations confirm the stability of protein-ligand complexes. Biolayer interferometry assays reveal P184-1419 exhibits stronger binding affinity (KD = 151 μM) than Vemurafenib (KD = 437 μM). These findings suggest P184-1419 is a promising lead compound against BRAF(V600E), offering potential for future development of more effective cancer therapies.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 5-6","pages":"e2400372"},"PeriodicalIF":2.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310149","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":"Drug Search and Design Considering Cell Specificity of Chemically Induced Gene Expression Profiles for Disease-Associated Tissues.","authors":"Chikashige Yamanaka, Michio Iwata, Kazuma Kaitoh, Yoshihiro Yamanishi","doi":"10.1002/minf.2444","DOIUrl":"10.1002/minf.2444","url":null,"abstract":"<p><p>The use of omics data, including gene expression profiles, has recently gained increasing attention in drug discovery. Omics-based drug searches and designs are often based on the correlations between chemically induced and disease-induced gene expression profiles; however, the cell specificity has not been considered. In this study, we designed a novel computational method for drug search and design using cell-specific correlations between drugs and diseases. A data completion technique allowed the characterization of cell-specific gene expression patterns in diseased cells. This proposed method was applied to search for drug candidates and generate new chemical structures for gastric cancer and atopic dermatitis. The results of drug search demonstrated that compounds with diverse chemical structures were detected and were associated with target diseases at the molecular pathway levels. The results of drug design also demonstrated that newly generated compounds were reasonable in terms of the reproducibility of registered drugs. The proposed method is expected to be useful for omics-based drug discovery.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"44 5-6","pages":"e2444"},"PeriodicalIF":2.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12188700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144485147","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}