Journal of Cheminformatics最新文献

Automatic extraction of molecules from scientific literature plays a crucial role in accelerating research across fields ranging from drug discovery to materials science. Patent documents, in particular, contain molecular information in visual form, which is often inaccessible through traditional text-based searches. In this work, we introduce SubGrapher, a method for the visual fingerprinting of molecule and Markush structure images. Unlike conventional Optical Chemical Structure Recognition (OCSR) models that attempt to reconstruct full molecular graphs, SubGrapher focuses on extracting fingerprints directly from images. Using learning-based instance segmentation, SubGrapher identifies functional groups and carbon backbones, constructing a substructure-based fingerprint that enables the retrieval of molecules and Markush structures. Our approach is evaluated against state-of-the-art OCSR and fingerprinting methods, demonstrating superior retrieval performance and robustness across diverse molecule and Markush structure depictions. The benchmark datasets, models, and inference code are publicly available..

Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to quantify the ease of synthesis for virtual molecules. Despite their utility, existing synthetic accessibility tools have notable limitations: they overlook compound purchasability, lack physical interpretability, and often rely on imperfect computer-aided synthesis planning algorithms. We introduce MolPrice, an accurate and fast model for molecular price prediction. Utilizing self-supervised contrastive learning, MolPrice autonomously generates price labels for synthetically complex molecules, enabling the model to generalize to molecules beyond the training distribution. Our results show that MolPrice reliably assigns higher prices to synthetically complex molecules than to readily purchasable ones, effectively distinguishing different levels of synthetic accessibility. Furthermore, MolPrice achieves competitive performance on literature benchmarks for synthetic accessibility. To demonstrate its practical utility, we conduct a virtual screening case study, illustrating how MolPrice successfully identifies purchasable molecules from a large candidate library. MolPrice bridges the gap between generative molecular design and real-world feasibility by integrating cost-awareness into synthetic accessibility assessment, making it a powerful model to accelerate molecular discovery.

Compared to monotherapy, drug combinations exhibit stronger efficacy, fewer side effects, and lower drug resistance in cancer treatment. However, traditional wet-lab methods for screening synergistic drug combinations are both costly and inefficient. Lately, the development of various drug synergy methods has been promoted by the emergence of multiple drug synergy databases. Many of these methods use multimodal data and achieve good results. However, if various modalities of data is given equal consideration without taking into account the differences in features between the two modalities, this may lead to less effective multi-modal learning. We propose a multi-modal contrastive learning method for drug synergy prediction, named MCDSP. Specifically, MCDSP extracts entity embedding features of drugs and cell lines from heterogeneous graphs, while leveraging molecular fingerprints and gene expression features as biomolecular features for drugs and cell lines. These two different types of features serve as two types of modality information. Under the guided of single modality prediction tasks, we evaluated the relevant information of each modality. Through contrastive learning, the prediction bias of the two modalities are reduced, which obtain improved quality of multi-modal feature. Experiments show that MCDSP outperforms baseline methods on large datasets, and it performs well in handling unknown drug combinations and cell lines. MCDSP has demonstrated significant effectiveness in predicting drug synergy.

The concept of contrastive explanations originating from human reasoning is used in explainable artificial intelligence. In machine learning, contrastive explanations relate alternative prediction outcomes to each other involving the identification of features leading to opposing model decisions. We introduce a methodological framework for deriving contrastive explanations for machine learning models in chemistry to systematically generate intuitive explanations of predictions in high-dimensional feature spaces. The molecular contrastive explanations (MolCE) methodology explores alternative model decisions by generating virtual analogues of test compounds through replacements of molecular building blocks and quantifies the degree of “contrastive shifts” resulting from changes in model probability distributions. In a proof-of-concept study, MolCE was applied to explain selectivity predictions of ligands of D2-like dopamine receptor isoforms.

The widespread adoption of open-source cheminformatics toolkits remains constrained by technical implementation barriers, including complex installation procedures, dependency management, and integration challenges. Here, we present Cheminformatics Microservice V3, a significant update to the existing platform that provides unified programmatic access to cheminformatics libraries, including RDKit, Chemistry Development Kit (CDK), and Open Babel through a RESTful API framework. This latest version features a newly developed, interactive web-based frontend built with React, providing users with an intuitive graphical interface for manipulating and analysing chemical structures. The frontend supports essential cheminformatics operations, including structure editing, PubChem database integration, batch molecular processing, and standardised InChI/RInChI identifier generation. The microservice V3 addresses critical accessibility barriers in computational chemistry by providing researchers with immediate access to analytical tools, eliminating the need for specialised technical expertise or complex software installations. This approach facilitates reproducible research workflows and broadens the utilisation of cheminformatics methodologies across interdisciplinary research communities. The platform is publicly accessible at https://app.naturalproducts.net, and the complete source code and documentation are available on GitHub.

In drug development, managing interactions such as drug–drug, drug–disease, and drug–nutrient is critical for ensuring the safety and efficacy of pharmacological treatments. These interactions often overlap, forming a complex, interconnected landscape that necessitates accurate prediction to improve patient outcomes and support evidence-based care. Recent advances in artificial intelligence (AI), powered by large-scale datasets (e.g., DrugBank, TWOSIDES, SIDER), have significantly enhanced interaction prediction. Machine learning, deep learning, and graph-based models show great promise, but challenges persist, including data imbalance, noisy sources, Limited explainability, and underrepresentation of certain types of interactions. This systematic review of 147 studies (2018–2024) is the first to comprehensively map AI applications across major interaction types. We present a detailed taxonomy of models and datasets, emphasizing the growing roles of large language models and knowledge graphs in overcoming key limitations. Their integration—alongside explainable AI tools—enhances transparency, paving the way for AI-driven systems that proactively mitigate adverse interactions. By identifying the most promising approaches and critical research gaps, this review lays the groundwork for advancing more robust, interpretable, and personalized models for drug interaction prediction.

The metabolic stability of a drug is a crucial determinant of its pharmacokinetic properties, including clearance, half-life, and oral bioavailability. Accurate predictions of metabolic stability can significantly streamline the drug discovery process. In this study, we present MetaboGNN, an advanced model for predicting liver metabolic stability based on Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). Using a high-quality dataset from the 2023 South Korea Data Challenge for Drug Discovery, which comprises 3,498 training molecules and 483 test molecules, we presented molecular structures as graphs to capture the intricate structural relationships that influence metabolic stability. A GCL-driven pretraining step was employed to enhance model generalizability by learning robust, transferable graph-level representations. Notably, incorporating interspecies differences between human liver microsomes (HLM) and mouse liver microsomes (MLM) further improved predictive accuracy, achieving Root Mean Square Error (RMSE) values of 27.91 (HLM) and 27.86 (MLM), both expressed as the percentage of parent compound remaining after a 30-min incubation. Compared to traditional approaches, MetaboGNN demonstrates superior predictive performance and highlights the importance of considering interspecies enzymatic variations. In addition, attention-based analysis identified key molecular fragments associated with metabolic stability, highlighting chemically meaningful structural determinants. These findings establish MetaboGNN as a powerful tool for metabolic stability prediction, supporting more efficient lead optimization processes in drug discovery.