Articles
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Citation: Journal of Cheminformatics 2022 14:13
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Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles
Chemical–genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances...
Citation: Journal of Cheminformatics 2022 14:12 -
Application of deep metric learning to molecular graph similarity
Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, w...
Citation: Journal of Cheminformatics 2022 14:11 -
MolData, a molecular benchmark for disease and target based machine learning
Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular pa...
Citation: Journal of Cheminformatics 2022 14:10 -
DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions
Adverse drug-drug interaction (DDI) is a major concern to polypharmacy due to its unexpected adverse side effects and must be identified at an early stage of drug discovery and development. Many computational ...
Citation: Journal of Cheminformatics 2022 14:9 -
PSnpBind: a database of mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow
A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. The...
Citation: Journal of Cheminformatics 2022 14:8 -
GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations
In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introd...
Citation: Journal of Cheminformatics 2022 14:7 -
Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features
Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected ...
Citation: Journal of Cheminformatics 2022 14:6 -
Sequence-based prediction of protein binding regions and drug–target interactions
Identifying drug–target interactions (DTIs) is important for drug discovery. However, searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed...
Citation: Journal of Cheminformatics 2022 14:5 -
Machine learning approaches to optimize small-molecule inhibitors for RNA targeting
In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold....
Citation: Journal of Cheminformatics 2022 14:4 -
LEADD: Lamarckian evolutionary algorithm for de novo drug design
Given an objective function that predicts key properties of a molecule, goal-directed de novo molecular design is a useful tool to identify molecules that maximize or minimize said objective function. Nonethel...
Citation: Journal of Cheminformatics 2022 14:3 -
Uncertainty-aware prediction of chemical reaction yields with graph neural networks
In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. ...
Citation: Journal of Cheminformatics 2022 14:2 -
HobPre: accurate prediction of human oral bioavailability for small molecules
Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of comput...
Citation: Journal of Cheminformatics 2022 14:1 -
TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids
In this work, we introduce TorsiFlex, a user-friendly software written in Python 3 and designed to find all the torsional conformers of flexible acyclic molecules in an automatic fashion. For the mapping of the t...
Citation: Journal of Cheminformatics 2021 13:100 -
Processing binding data using an open-source workflow
The thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and Tm shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (...
Citation: Journal of Cheminformatics 2021 13:99 -
Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms
Rapid solvent selection is of great significance in chemistry. However, solubility prediction remains a crucial challenge. This study aimed to develop machine learning models that can accurately predict compou...
Citation: Journal of Cheminformatics 2021 13:98 -
ChemTables: a dataset for semantic classification on tables in chemical patents
Chemical patents are a commonly used channel for disclosing novel compounds and reactions, and hence represent important resources for chemical and pharmaceutical research. Key chemical data in patents is ofte...
Citation: Journal of Cheminformatics 2021 13:97 -
Splitting chemical structure data sets for federated privacy-preserving machine learning
With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to hav...
Citation: Journal of Cheminformatics 2021 13:96 -
Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development
Predicting compound–protein interactions (CPIs) is of great importance for drug discovery and repositioning, yet still challenging mainly due to the sparse nature of CPI matrixes, resulting in poor generalizat...
Citation: Journal of Cheminformatics 2021 13:95 -
MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning
The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative...
Citation: Journal of Cheminformatics 2021 13:94 -
Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in ...
Citation: Journal of Cheminformatics 2021 13:93 -
The effect of noise on the predictive limit of QSAR models
A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed i...
Citation: Journal of Cheminformatics 2021 13:92 -
Development of a chemogenomics library for phenotypic screening
With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of nov...
Citation: Journal of Cheminformatics 2021 13:91 -
Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We...
Citation: Journal of Cheminformatics 2021 13:90 -
DockStream: a docking wrapper to enhance de novo molecular design
Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-...
Citation: Journal of Cheminformatics 2021 13:89 -
Molecular generation by Fast Assembly of (Deep)SMILES fragments
In recent years, in silico molecular design is regaining interest. To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecu...
Citation: Journal of Cheminformatics 2021 13:88 -
Deep scaffold hopping with multimodal transformer neural networks
Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecu...
Citation: Journal of Cheminformatics 2021 13:87 -
Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion
In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extrac...
Citation: Journal of Cheminformatics 2021 13:86 -
DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo des...
Citation: Journal of Cheminformatics 2021 13:85 -
MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra
Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures o...
Citation: Journal of Cheminformatics 2021 13:84 -
QSPR modeling of selectivity at infinite dilution of ionic liquids
The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and lab...
Citation: Journal of Cheminformatics 2021 13:83 -
Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning
Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the r...
Citation: Journal of Cheminformatics 2021 13:82 -
The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
Structure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major chal...
Citation: Journal of Cheminformatics 2021 13:81 -
Individual and collective human intelligence in drug design: evaluating the search strategy
In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to sol...
Citation: Journal of Cheminformatics 2021 13:80 -
Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier
We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an...
Citation: Journal of Cheminformatics 2021 13:79 -
Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons
Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago...
Citation: Journal of Cheminformatics 2021 13:78 -
Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning
Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate ...
Citation: Journal of Cheminformatics 2021 13:77 -
Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization
Chemical diversity is one of the key term when dealing with machine learning and molecular generation. This is particularly true for quantum chemical datasets. The composition of which should be done meticulou...
Citation: Journal of Cheminformatics 2021 13:76 -
FP-ADMET: a compendium of fingerprint-based ADMET prediction models
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on mach...
Citation: Journal of Cheminformatics 2021 13:75 -
How can SHAP values help to shape metabolic stability of chemical compounds?
Computational methods support nowadays each stage of drug design campaigns. They assist not only in the process of identification of new active compounds towards particular biological target, but also help in ...
Citation: Journal of Cheminformatics 2021 13:74 -
GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics
Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the f...
Citation: Journal of Cheminformatics 2021 13:73 -
ProLIF: a library to encode molecular interactions as fingerprints
Interaction fingerprints are vector representations that summarize the three-dimensional nature of interactions in molecular complexes, typically formed between a protein and a ligand. This kind of encoding ha...
Citation: Journal of Cheminformatics 2021 13:72 -
DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DT...
Citation: Journal of Cheminformatics 2021 13:71 -
Using informative features in machine learning based method for COVID-19 drug repurposing
Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases wo...
Citation: Journal of Cheminformatics 2021 13:70 -
A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling
Reliable uncertainty quantification for statistical models is crucial in various downstream applications, especially for drug design and discovery where mistakes may incur a large amount of cost. This topic ha...
Citation: Journal of Cheminformatics 2021 13:69 -
Investigation of pharmacological mechanism of natural product using pathway fingerprints similarity based on “drug-target-pathway” heterogenous network
Natural products from traditional medicine inherit bioactivity from their source herbs. However, the pharmacological mechanism of natural products is often unclear and studied insufficiently. Pathway fingerpri...
Citation: Journal of Cheminformatics 2021 13:68 -
Selecting lines for spectroscopic (re)measurements to improve the accuracy of absolute energies of rovibronic quantum states
Improving the accuracy of absolute energies associated with rovibronic quantum states of molecules requires accurate high-resolution spectroscopy measurements. Such experiments yield transition wavenumbers fro...
Citation: Journal of Cheminformatics 2021 13:67 -
2D SIFt: a matrix of ligand-receptor interactions
Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of...
Citation: Journal of Cheminformatics 2021 13:66 -
PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucia...
Citation: Journal of Cheminformatics 2021 13:65 -
Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop
We report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academ...
Citation: Journal of Cheminformatics 2021 13:64