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Augmented ant colony algorithm for virtual drug discovery
Docking is a fundamental problem in computational biology and drug discovery that seeks to predict a ligand’s binding mode and affinity to a target...
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SBDD and Its Challenges
Proteins are the important biological macromolecules that are targeted by most of the existing drugs. SBDD play a critical role in design of...
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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....
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The C++ programming language in cheminformatics and computational chemistry
This paper describes salient features of the C++ programming language and its programming ecosystem, with emphasis on how the language affects...
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Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion
Data-independent acquisition–all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants...
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Chemical approaches to cryopreservation
Cryopreservation of cells and biologics underpins all biomedical research from routine sample storage to emerging cell-based therapies, as well as...
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CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space
AbstractChemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only...
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MORTAR: a rich client application for in silico molecule fragmentation
Develo** and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule...
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Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge
The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational...
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Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the...
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Making the collective knowledge of chemistry open and machine actionable
Large amounts of data are generated in chemistry labs—nearly all instruments record data in a digital form, yet a considerable proportion is also...
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Convolutional neural networks for sensitive identification of tea species using electrochemical sensors
A fast and precise convolutional neural network (CNN) framework was built for classifying tea species using low-cost electrochemical profiles of...
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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...
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MDSuite: comprehensive post-processing tool for particle simulations
Particle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available...
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Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut
Data-independent acquisition (DIA) for liquid chromatography tandem mass spectrometry (LC-MS/MS) can improve the depth and reproducibility of the...
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Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription...
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Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain...
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