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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...
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...
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 ...
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...
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 ...
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...
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...
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...
The original article was published in Journal of Cheminformatics 2023 15:20
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...
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...
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...
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...
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...
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...
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...
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...
Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, ...
The Correction to this article has been published in Journal of Cheminformatics 2023 15:30
Particle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed afte...
Molecular similarity search is an often-used method in drug discovery, especially in virtual screening studies. While simple one- or two-dimensional similarity metrics can be applied to search databases contai...
Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of th...
The identification of drug/compound–target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been developed. As a relatively new data-driven paradi...
The Correspondence to this article has been published in Journal of Cheminformatics 2023 15:14
Citations are an essential aspect of research communication and have become the basis of many evaluation metrics in the academic world. Some see citation counts as a mark of scientific impact or even quality, ...
The Editorial to this article has been published in Journal of Cheminformatics 2023 15:15
Quantifying uncertainty in machine learning is important in new research areas with scarce high-quality data. In this work, we develop an explainable uncertainty quantification method for deep learning-based m...
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mut...
In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretrainin...
This article documents enu, a freely-downloadable, open-source and stand-alone program written in C++ for the enumeration of the constitutional isomers and stereoisomers of a molecular formula. The program relies...
Structure-constrained molecular generation is a promising approach to drug discovery. The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source mol...
The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for...
Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic ...
Ubiquitin-specific-processing protease 7 (USP7) is a promising target protein for cancer therapy, and great attention has been given to the identification of USP7 inhibitors. Traditional virtual screening meth...
Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the an...
With the ongoing rapid growth of publicly available ligand–protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data...
Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over ...
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequence...
Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important rol...
The relationships between molecular structures and their properties are subtle and complex, and the properties of odor are no exception. Molecules with similar structures, such as a molecule and its optical is...
This article demonstrates how to create Chemical Space Networks (CSNs) using a Python RDKit and NetworkX workflow. CSNs are a type of network visualization that depict compounds as nodes connected by edges, de...
A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integr...
Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domai...
Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior develop...
In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed...
We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with speakers f...
The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid th...
While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, ...
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of hig...
Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properti...
