Molecular Informatics最新文献

Foundation and multimodal models are rapidly becoming a core methodology in molecular informatics, particularly for drug discovery, by leveraging large-scale pretraining across sequences, graphs, 3D structures, and text. This mini-review provides practical guidance on when these models help, how to choose representations and data, and how to design pretraining and adaptation pipelines for real-world use. We clarify what qualifies as a foundation model in chemistry; compare chemical language models, graph-based architectures, and 3D equivariant networks; review multimodal strategies that connect molecules with proteins, pockets, and natural language; and summarize diffusion-based generative modeling. We also emphasize rigorous evaluation, discussing realistic splitting protocols, distribution shift, activity cliffs, uncertainty calibration, and conformal prediction in the context of widely used benchmarks.

The growing number and size of DNA-encoded libraries (DELs), together with the vast space of possible DEL designs, demand interpretable and scalable criteria for selecting which libraries to construct and screen against a given target. An ideal target-focused DEL shows both strong similarity with an active reference compound collection and high intra-DEL diversity. Chemography with Generative Topographic Mapping (GTM) was shown to be a promising approach for selecting DELs, offering both intuitive visualization and fast quantitative analysis scalable to thousands of DEL designs. This is achieved by defining each library by a "stand-alone" vector, the comparison of which precludes costly pairwise inter-molecular similarity calculations. However, the extent to which such "stand-alone" (SA) approaches in general, and GTM-derived SA metrics in particular, recover DELs that are reference-proximal and chemically diverse as evaluated by conventional compound pair-matching (CP) metrics in the initial descriptor space remains insufficiently characterized. In this article, the comparative analysis of the Morgan count fingerprint-based chemical-library similarity versus GTM-derived metrics, using 100 diverse DEL subsets and a reference set of compounds tested against cyclin-dependent kinase 2 (CDK2) from ChEMBL, was performed. GTM-based SA metrics provide robust approximations for "gold standard" molecular descriptor space CP metrics for DEL selection: Spearman rank correlations fall in the 0.6-0.7 range. Our results demonstrate that GTM helps to identify DELs that best span the reference space according to same "gold standard" molecular descriptor space metrics: SA GTM-driven rankings of libraries achieve enrichment factors at 5% (EF5%) of 4-12 (in terms of finding "gold standard" top libraries within the 5% best ranked by GTM)-always picking 2 out of the top 3 libraries. The accompanying two-dimensional landscapes make intra- and interlibrary diversity visually accessible, supporting rapid, interpretable screening of alternative DEL designs. Collectively, these results position GTM as an efficient tool for chemical-library similarity assessment and target-focused DEL selection.

Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This work presents a computational methodology for searching for pathways in reaction networks which is based on integer linear programing and the modeling of reaction networks by directed hypergraphs. Often multiple pathways fit the given search criteria. To rank them, we develop an objective function based on physical arguments maximizing the probability of the pathway. We furthermore develop an automated pipeline to estimate the energy barriers of individual reactions in reaction networks. Combined, the methodology facilitates flexible and kinetically informed pathway investigations on large reaction networks by computational means, even for networks coming without kinetic annotation, such as those created via generative approaches for expanding molecular spaces. To demonstrate the methodology, we apply it on a chemical reaction network generated from 2-hydroxyethanenitrile, water, and ammonia, where we search for pathways to glycine and 2-hydroxyethanoic acid using the input molecules as precursors.

Human 5-lipoxygenase (5-LOX) plays a crucial role in the biosynthesis of leukotrienes (LTs). Therefore, 5-LOX inhibitors are designed as effective agents for the treatment of several diseases such as asthma, cardiovascular disorders, allergies, and cancer. Insights into crystal structures of several 5-LOX isoforms have revealed that this protein adopts two different conformations (open/closed) through modulation of its Hα2 and arched helix regions, which are conditioned by the presence or absence of ligand in the active site; moreover, these structures are incomplete in regions critical for ligand binding. To advance the design of 5-LOX inhibitors, we developed a computational procedure to reconstruct the first full-length open conformation structure of 5-LOX complexed with chelating inhibitor within the active site. Dynamic simulations and protein model validation confirmed the quality of our model, which was subsequently used for docking analyses and culminated in the development of a structure-based pharmacophore model. These computational studies might constitute powerful tools for rationally designing and identifying novel 5-LOX iron chelator inhibitors.

Rapidly progressive Alzheimer's disease (rpAD) is a rare but severe form of Alzheimer's disease characterized by accelerated cognitive decline and limited therapeutic options. Conventional anti-amyloid-β interventions have shown little success due to poor target specificity, neurotoxicity, and lack of efficacy, underscoring the need for novel therapeutic strategies. This study aimed to identify and prioritize molecular targets associated with rpAD by investigating the protein interactome of amyloid-β (Aβ₄₂) using integrative computational approaches. Functional enrichment, protein-protein interaction network analysis, and community clustering revealed that rpAD-specific Aβ₄₂ interactors were predominantly involved in mitochondrial bioenergetics, redox regulation, and cytoskeletal stability, pathways central to neuronal survival and synaptic function. Molecular docking identified fumarate hydratase, carbonyl reductase 1, and the F-actin capping protein as high-affinity interactors of Aβ₄₂, linking these proteins to energy failure, oxidative stress, and synaptic dysfunction. Virtual screening of a therapeutic drug library against fumarate hydratase identified several compounds with strong binding affinities, among which quinestrol, estradiol benzoate, norethindrone, tamibarotene, drospirenone, and ketanserin emerged as lead candidates. Pharmacokinetic profiling, including ADMET modeling, confirmed their blood-brain barrier permeability and drug-likeness, supporting their potential as central nervous system active agents. Together, this work highlights key molecular targets in rpAD and proposes repurposed, pharmacologically diverse compounds with multitarget neuroprotective potential. By utilizing in silico analysis, the study provides a rational framework for target discovery and drug prioritization in rpAD, offering a foundation for future experimental validation and the development of translational research.

Diversity and properties of ring systems contained in small molecules are of high interest for applications such as drug discovery and materials science. In the present work, we extract, analyze, and classify ring systems found in small-molecule compounds of open-access databases such as PubChem, ChEMBL, DrugCentral, IUPHAR, and LOTUS. We also developed a classification taxonomy of frequently found ring systems using an Open Biomedical Ontologies (OBO)-format ontology. Open-access software was used to automate the classification of compounds into their respective ring system classes. As an example, the natural product compounds of the LOTUS database were classified and are available as an open-access ontology dataset.

Yield prediction in catalytic reactions is essential for improving chemical process efficiency and product quality. Ligands significantly influence reactivity and selectivity, highlighting the need for descriptors that accurately capture their structural and electronic properties. In this study, we focus on infrared (IR) spectra, which reflects molecular vibrational modes, and propose novel descriptors based on wavenumber information. We evaluated the predictive performance of these descriptors using two datasets: direct Pd-catalyzed arylation and Suzuki-Miyaura coupling reactions. The wavenumber-based IR descriptors outperformed conventional molecular descriptors and structural fingerprints (one-hot encoding, Mordred, MACCS, Morgan fingerprint, RDKit, and density functional theory). Notably, descriptors limited to the fingerprint region (0-1700 cm^-1) effectively captured key molecular features, contributing to both high prediction accuracy and improved chemical interpretability. Our results indicate that IR-based descriptors can achieve strong generalization performance even with small datasets. This approach offers a promising strategy for redefining reaction condition spaces and enhancing the interpretability of predictive models, thereby supporting more informed experimental design.

Considering the limited efficacy of existing pharmacotherapies for brain tumors, the development of accurate predictive models is essential for advancing neuro-oncology treatment strategies. In this article, we introduce a drug response prediction model, DeepMoDRP, specifically designed for brain cancer. This model integrates genomic, transcriptomic, and epigenomic data from various brain tumor cell lines, including low-grade glioma, glioblastoma multiforme, and diffuse large B-cell lymphoma. To address the high-dimensional complexity inherent in gene expression and copy number variations within cell line data, we have integrated sparse autoencoders (AEs) and denoising AEs to reduce noise and redundancy. Meanwhile, one-dimensional convolutional neural networks are utilized to process the low-dimensional mutation and DNA methylation data. Additionally, a multiscale graph neural network is implemented to handle the drug-related data. Finally, fully connected networks are employed to generate predictions of drug responses. A series of experiments were conducted utilizing a brain tumor dataset that was extracted and curated from public databases. The experimental results demonstrate that the proposed DeepMoDRP outperforms the performance of state-of-the-art pan-cancer baseline models in predicting drug responses for brain tumors. The downstream analysis indicates that the DeepMoDRP holds significant promise for the treatment of brain tumors.

Accurately predicting the fraction unbound in plasma (f_up) from chemical structures is essential for understanding pharmacokinetic characteristics during the early stage of drug discovery. This prediction serves as a valuable tool for minimizing late-stage setbacks and refining subsequent screening processes. Conventional approaches often rely on complex computational methodologies that may require extensive descriptor sets, resulting in opaque models with limited interpretability. In this study, we applied the read-across strategy in combination with traditional quantitative structure-property relationship to predict f_up while minimizing descriptor complexity. Our method employs interpretable models (regression and classification), facilitating insight into the underlying structure-property relationships governing plasma protein binding. Through comprehensive validation and comparison with different machine learning methods, we demonstrated the superior predictive performance of quantitative read-across structure-property relationship multiple linear regression and classification-based read-across structure-property relatonship respectively. support vector classifier models across diverse chemical compounds. This approach offers a valuable tool for predicting f_up in the process of drug discovery. Overall, this study aims to advance the field of pharmacokinetic modeling by applying the read-across strategy that improves predictive power with interpretability. By elucidating the complex relationship between chemical structures and f_up, our best models have the potential to formulate more rational drug design approaches, ultimately contributing to the development of more effective therapeutics.

The pharmacokinetic profile of a potential drug is largely determined by its metabolic stability, which reflects its susceptibility to biotransformation. Metabolic stability data allow one to assess the therapeutic value of a compound and its toxicological risk. This assesment relies primarily on pharmacokinetic parameters, particularly half-life (t_1/2) and clearance (CL), which are typically determined using in vitro systems including hepatocytes and liver microsomal fractions. Using the publicly available ChEMBL v. 35 and PubChem databases, we collected over 8000 chemical compounds with experimental intrinsic CL and/or half-life data from liver microsome assays obtained in mice, rats, and humans. Different thresholds were applied to differentiate the stable and unstable molecules. The Naive Bayesian classifier with MNA (Multilevel Neighborhoods of Atoms) descriptors and Self-Consistent Extreme Classifier (SCEC) with QNA (Quantitative Neighborhoods of Atoms) descriptors were used for creating classification models. The accuracy (AUC) of most classification models exceeded 0.85. Self-Consistent Regression was used to create quantitative models. The coefficient of determination of the regression models varied from 0.35 (rat, t_1/2) to 0.7 (human, CL_int). These models were integrated into the freely available web application MetaStab-Analyzer, which provides a unique combination of qualitative (stable/unstable/moderate) and quantitative predictions for three species. A key feature of the application is the providing of numerical metrics for each prediction, which increases its interpretability. This combination of innovative algorithms (SCR and SCEC), dual qualitative-quantitative assessment, and a user-friendly interface is not available in any existing tool. MetaStab-Analyzer is freely available at https://www.way2drug.com/metastab/.