Artificial intelligence-aiding lab-on-a-chip workforce designed oral [3.1.0] bi and [4.2.0] tricyclic catalytic interceptors inhibiting multiple SARS-CoV-2 protomers assisted by double-shell deep learning

RSC Advances, Journal Year: 2024, Volume and Issue: 14(37), P. 26897 - 26910

Published: Jan. 1, 2024

Deep learning-integrated lab-on-a-chip in designing oral [3.1.0] bi and [4.2.0] tricyclic interceptors inhibiting multiple SARS-CoV-2 protomers.

Language: Английский

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A Machine Learning Approach for Efficiently Predicting Polymer Aging from UV–Vis Spectra

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 14, 2024

This research has introduced an innovative approach that proficiently forecasts the alterations in ultraviolet-visible spectroscopy (UV-Vis) of polymer solutions during aging effect. method combines readily accessible feature descriptors with classical machine learning (ML) algorithms. Traditional spectral measurements, while precise analyzing physical properties, are limited by their cost and efficiency. Therefore, this paper introduces a utilizes wavelength blue (

Language: Английский

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Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules

Machine Learning Science and Technology, Journal Year: 2024, Volume and Issue: 5(4), P. 045023 - 045023

Published: Oct. 15, 2024

Abstract We present machine learning models based on kernel-ridge regression for predicting x-ray photoelectron spectra of organic molecules originating from the K -shell ionization energies carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. constructed training dataset through high-throughput calculations core-electron binding (CEBEs) 12 880 small in bigQM7 ω dataset, employing Δ-SCF formalism coupled with meta-GGA-DFT a variationally converged basis set. The are cost-effective, as they require atomic coordinates molecule generated using universal force fields while estimating target-level CEBEs corresponding to DFT-level equilibrium geometry. explore transfer by utilizing environment feature vectors learned graph neural network framework regression. Additionally, we enhance accuracy within Δ-machine leveraging inexpensive baseline derived Kohn–Sham eigenvalues. When applied 208 combinatorially substituted uracil larger than those set, our analyses suggest that may not provide quantitatively accurate predictions but offer strong linear correlation relevant virtual screening. Python module, cebeconf , facilitate further explorations.

Language: Английский

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Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design materials informatics research using deep neural networks. Matini-Net provides the flexibility to feature-based, graph-based, combinations of these models, accommodating both single- multimodal model architectures. For validation, performed performance evaluation on MatBench benchmarking dataset five properties, targeting types regression architectures that can be designed Matini-Net. When applied each material property datasets, best various exhibited

Language: Английский

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Recent progress of black phosphorene from preparation to diversified bio-/chemo-nanosensors and their challenges and opportunities for comprehensive health

Microchimica Acta, Journal Year: 2024, Volume and Issue: 191(12)

Published: Nov. 29, 2024

Language: Английский

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