Articles
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Citation: Journal of Cheminformatics 2023 15:70
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PyL3dMD: Python LAMMPS 3D molecular descriptors package
Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the...
Citation: Journal of Cheminformatics 2023 15:69 -
Correction: Reconstruction of lossless molecular representations from fingerprints
Citation: Journal of Cheminformatics 2023 15:68 -
Explaining compound activity predictions with a substructure-aware loss for graph neural networks
Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructur...
Citation: Journal of Cheminformatics 2023 15:67 -
The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the Univer...
Citation: Journal of Cheminformatics 2023 15:66 -
Force field-inspired transformer network assisted crystal density prediction for energetic materials
Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they c...
Citation: Journal of Cheminformatics 2023 15:65 -
PINNED: identifying characteristics of druggable human proteins using an interpretable neural network
The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to ide...
Citation: Journal of Cheminformatics 2023 15:64 -
TB-IECS: an accurate machine learning-based scoring function for virtual screening
Machine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of f...
Citation: Journal of Cheminformatics 2023 15:63 -
Improving reproducibility and reusability in the Journal of Cheminformatics
Citation: Journal of Cheminformatics 2023 15:62 -
A comparison of approaches to accessing existing biological and chemical relational databases via SPARQL
Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database ...
Citation: Journal of Cheminformatics 2023 15:61 -
ProfhEX: AI-based platform for small molecules liability profiling
Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug’s adverse effects in the early stages is necessary to minimize health risks to p...
Citation: Journal of Cheminformatics 2023 15:60 -
Adaptive language model training for molecular design
The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a...
Citation: Journal of Cheminformatics 2023 15:59 -
RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking
Retrosynthesis is an important task in organic chemistry. Recently, numerous data-driven approaches have achieved promising results in this task. However, in practice, these data-driven methods might lead to s...
Citation: Journal of Cheminformatics 2023 15:58 -
Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation
Three-dimensional (3D) conformations of a small molecule profoundly affect its binding to the target of interest, the resulting biological effects, and its disposition in living organisms, but it is challengin...
Citation: Journal of Cheminformatics 2023 15:57 -
Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data
The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training dat...
Citation: Journal of Cheminformatics 2023 15:56 -
Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a c...
Citation: Journal of Cheminformatics 2023 15:55 -
Harnessing Shannon entropy-based descriptors in machine learning models to enhance the prediction accuracy of molecular properties
Accurate prediction of molecular properties is essential in the screening and development of drug molecules and other functional materials. Traditionally, property-specific molecular descriptors are used in ma...
Citation: Journal of Cheminformatics 2023 15:54 -
Combatting over-specialization bias in growing chemical databases
Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive...
Citation: Journal of Cheminformatics 2023 15:53 -
UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis
Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and acc...
Citation: Journal of Cheminformatics 2023 15:52 -
OWSum: algorithmic odor prediction and insight into structure-odor relationships
We derived and implemented a linear classification algorithm for the prediction of a molecule’s odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules a...
Citation: Journal of Cheminformatics 2023 15:51 -
DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists
Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modu...
Citation: Journal of Cheminformatics 2023 15:50 -
Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation
It is insightful to report an estimator that describes how certain a model is in a prediction, additionally to the prediction alone. For regression tasks, most approaches implement a variation of the ensemble ...
Citation: Journal of Cheminformatics 2023 15:49 -
In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences
Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-cons...
Citation: Journal of Cheminformatics 2023 15:48 -
Exploring QSAR models for activity-cliff prediction
Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs (ACs). It has been hypoth...
Citation: Journal of Cheminformatics 2023 15:47 -
Biomedical data analyses facilitated by open cheminformatics workflows
Citation: Journal of Cheminformatics 2023 15:46 -
Investigation of chemical structure recognition by encoder–decoder models in learning progress
Descriptor generation methods using latent representations of encoder–decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it...
Citation: Journal of Cheminformatics 2023 15:45 -
Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, th...
Citation: Journal of Cheminformatics 2023 15:44 -
MetaRF: attention-based random forest for reaction yield prediction with a few trails
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with hi...
Citation: Journal of Cheminformatics 2023 15:43 -
MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules
Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without...
Citation: Journal of Cheminformatics 2023 15:42 -
LinChemIn: SynGraph—a data model and a toolkit to analyze and compare synthetic routes
The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learn...
Citation: Journal of Cheminformatics 2023 15:41 -
VSFlow: an open-source ligand-based virtual screening tool
Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source c...
Citation: Journal of Cheminformatics 2023 15:40 -
qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data
High throughput screening (HTS) is widely used in drug discovery and chemical biology to identify and characterize agents having pharmacologic properties often by evaluation of large chemical libraries. Standa...
Citation: Journal of Cheminformatics 2023 15:39 -
Deep generative model for drug design from protein target sequence
Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substan...
Citation: Journal of Cheminformatics 2023 15:38 -
GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES
Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. Th...
Citation: Journal of Cheminformatics 2023 15:37 -
Interpreting vibrational circular dichroism spectra: the Cai•factor for absolute configuration with confidence
Vibrational circular dichroism (VCD) spectroscopy can generate the data required for the assignment of absolute configuration, but the spectra are hard to interpret. We have recorded VCD data for thirty pairs ...
Citation: Journal of Cheminformatics 2023 15:36 -
Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods
Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimiza...
Citation: Journal of Cheminformatics 2023 15:35 -
PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping
Toxicological evaluation of substances in regulation still often relies on animal experiments. Understanding the substances’ mode-of-action is crucial to develop alternative test strategies. Omics methods are ...
Citation: Journal of Cheminformatics 2023 15:34 -
DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increase...
Citation: Journal of Cheminformatics 2023 15:33 -
MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry
Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-thro...
Citation: Journal of Cheminformatics 2023 15:32 -
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects...
Citation: Journal of Cheminformatics 2023 15:31 -
Correction: Global reactivity models are impactful in industrial synthesis applications
Citation: Journal of Cheminformatics 2023 15:30 -
ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction
Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants...
Citation: Journal of Cheminformatics 2023 15:29 -
Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data
Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemic...
Citation: Journal of Cheminformatics 2023 15:28 -
Double-head transformer neural network for molecular property prediction
Existing molecular property prediction methods based on deep learning ignore the generalization ability of the nonlinear representation of molecular features and the reasonable assignment of weights of molecul...
Citation: Journal of Cheminformatics 2023 15:27 -
Reconstruction of lossless molecular representations from fingerprints
The simplified molecular-input line-entry system (SMILES) is the most prevalent molecular representation used in AI-based chemical applications. However, there are innate limitations associated with the intern...
Citation: Journal of Cheminformatics 2023 15:26 -
Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions
Published reports of chemical compounds often contain multiple machine-readable descriptions which may supplement each other in order to yield coherent and complete chemical representations. This publication p...
Citation: Journal of Cheminformatics 2023 15:25 -
DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning
Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. Wi...
Citation: Journal of Cheminformatics 2023 15:24 -
Notes on molecular fragmentation and parameter settings for a dissipative particle dynamics study of a C10E4/water mixture with lamellar bilayer formation
The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A “bottom-up” dec...
Citation: Journal of Cheminformatics 2023 15:23 -
UnCorrupt SMILES: a novel approach to de novo design
Generative deep learning models have emerged as a powerful approach for de novo drug design as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the fiel...
Citation: Journal of Cheminformatics 2023 15:22 -
Paths to cheminformatics: Q&A with Phyo Phyo Kyaw Zin
Citation: Journal of Cheminformatics 2023 15:21