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Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, ...
The original article was published in Journal of Cheminformatics 2025 17:130
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 ...
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, conta...
Metabolomics data analysis is a multifaceted process often constrained by limited data sharing and a lack of transparency, which hinders reproducibility of results. While existing bioinformatics tools address ...
Drug exposure, a key determinant of drug safety and efficacy, is governed by pharmacokinetic (PK) parameters such as volume of distribution (VDss), clearance (CL), half-life (t½), fraction unbound in plasma (f...
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 comb...
In this study, we present a pipeline for identifying novel ligands targeting the Tryptophan-Aspartate-Repeat domain 40 (WDR40) of Leucine-Rich Repeat Kinase 2 (LRRK2), a protein associated with Parkinson’s dis...
Advances in docking protocols have significantly enhanced the field of protein–protein interaction (PPI) modulation, with AlphaFold2 (AF2) and molecular dynamics (MD) refinements playing pivotal roles. This st...
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 ...
The widespread adoption of open-source cheminformatics toolkits remains constrained by technical implementation barriers, including complex installation procedures, dependency management, and integration chall...
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, f...
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 significa...
The Korea Chemical Bank (KCB) has generated a dataset containing metabolic stability data for approximately 4,000 compounds that have been tested on human and mouse liver microsomes. The first South Korea Data...
Deriving symbolic knowledge from trained deep learning models is challenging due to the lack of transparency in such models. A promising approach to address this issue is to couple a semantic structure with th...
Molecular representations of chirality, derived from latent space vectors (LSVs) of SMILES heteroencoders, were explored to train machine learning models to predict chiral properties, and were compared to conv...
Natural products provide a rich source of bioactive molecules for a variety of applications. Molecular fingerprints are the tool of choice for systematic large-scale studies of their structures. However, curre...
In recent years, the integration of Artificial Intelligence and Machine Learning methods with biochemical and biomedical research has revolutionized the field of toxicology, significantly advancing our underst...
Protein-protein interactions (PPIs) regulate essential biological processes through complex interfaces, with their dysfunction is associated with various diseases. Consequently, the identification of PPIs and ...
Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent ...
In this paper, we propose a robust deep-learning model based on a Quantitative Structure − Property Relationship (QSPR) approach for estimating the critical temperature (TC), critical pressure (PC), acentric f...
When data availability is limited, the prediction of properties through purely data-driven machine learning (ML) is challenging. Integrating physically-based modeling techniques into ML methods may lead to bet...
Retrosynthesis—the process of deconstructing complex molecules into simpler, more accessible precursors—is a cornerstone of drug discovery and material design. While machine learning has improved single-step r...
The Correction to this article has been published in Journal of Cheminformatics 2025 17:151
Cyclic peptides are promising drug candidates due to their ability to modulate intracellular protein–protein interactions, a property often inaccessible to small molecules. However, their typically poor membra...
Quantitative structure–activity relationship (QSAR) modeling has become a critical tool in drug design. Recently proposed Topological Regression (TR), a computationally efficient and highly interpretable QSAR ...
Bitter peptides (BPs), derived from the hydrolysis of proteins in food, play a crucial role in both food science and biomedicine by influencing taste perception and participating in various physiological proce...
Accurate chemical reaction prediction is critical for reducing both cost and time in drug development. This study introduces ReactionT5, a transformer-based chemical reaction foundation model pre-trained on th...
Inference of molecules with desired activities/properties is one of the key and challenging issues in cheminformatics and bioinformatics. For that purpose, our research group has recently developed a state-of-...
Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to the discontinuation of clinical trials and the withdrawal of drugs from the market. This study explores the appli...
Accurate prediction of toluene/water partition coefficients of neutral species is crucial in drug discovery and separation processes; however, data-driven modeling of these coefficients remains challenging due...
This editorial presents an analysis of the articles published in the Journal of Cheminformatics Special Issue “AI in Drug Discovery”. We review how novel machine learning developments are enhancing structural-bas...
Chemoinformatics is a rapidly advancing field that integrates chemistry, computer science, and data analysis to enhance the study and application of chemical systems. This interdisciplinary approach leverages ...
Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term “nanobody” is now more generally used...
Drug-induced liver injury (DILI) presents a significant challenge due to its complexity, small datasets, and severe class imbalance. While unsupervised pretraining is a common approach to learn molecular repre...
Chemical explosion accidents represent a significant threat to both human safety and environmental integrity. The accurate prediction of such incidents plays a pivotal role in risk mitigation and safety enhanc...
Accurate chemical structure resolution from textual identifiers such as names and CAS RN® is critical for computational modeling in chemistry and related fields. This paper introduces MoleculeResolver, an auto...
Generative artificial intelligence (GenAI) models have emerged as a transformative tool for addressing the complex challenges of drug discovery, enabling the design of structurally diverse, chemically valid, a...
Generating accurate molecular conformations hinges on sampling effectively from a high-dimensional space of atomic arrangements, which grows exponentially with system size. To ensure physically valid geometrie...
The original article was published in Journal of Cheminformatics 2025 17:103
Interactions of proteins with other molecules are often mediated by a set of critical binding motifs on their surfaces. Most traditional binder designs relied on motifs borrowed from known binder molecules, wh...
Methods for automatic chemical retrosynthesis have found recent success through the application of models traditionally built for natural language processing, primarily through transformer neural networks. The...
Identification is a major challenge in metabolomics due to the large structural diversity of metabolites. Tandem mass spectrometry is a reference technology for studying the fragmentation of molecules and char...
The human Ether-à-go-go-Related Gene (hERG) potassium channel is crucial for repolarizing the cardiac action potential and regulating the heartbeat. Molecules that inhibit this protein can cause acquired long ...
Advancements in cheminformatics have led to numerous methods for encoding molecules numerically. The choice of molecular representation impacts the accuracy and generalizability of learning algorithms applied ...
This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained using ligand-based representations. We p...
Metal ions, as abundant and vital cofactors in numerous proteins, are crucial for enzymatic activities and protein interactions. Given their pivotal role and catalytic efficiency, accurately and efficiently id...
Age-related diseases and syndromes result in poor quality of life and adverse outcomes, representing a challenge to healthcare systems worldwide. Several pharmacological interventions have been proposed to tar...
Finding optimal reaction conditions is crucial for chemical synthesis in the pharmaceutical and chemical industries. However, due to the vast chemical space, conducting experiments for all the possible combina...
For over half a century, computer-aided structural elucidation systems (CASE) for organic compounds have relied on complex expert systems with explicitly programmed algorithms. These systems are often computat...
The Correction to this article has been published in Journal of Cheminformatics 2025 17:114
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