Library

SEARCHING BY METOD OF NEURAL NETWORK MODELING OF CONDENSED BENZIMIDAZOLE DERIVATIVES WITH MULTI-TARGET RAGE-INHIBITORY ACTIVITY

Using a multitarget neural network model of RAGE-inhibitory activity, a consensus virtual screening of a library of new condensed benzimidazole derivatives was performed. Compounds with a essential RAGE-inhibitory effect have been found.

MedicineChemistryComputer Science
Source

Paper

Full text

PDF

SEARCHING BY METOD OF NEURAL NETWORK MODELING OF CONDENSED BENZIMIDAZOLE DERIVATIVES WITH MULTI-TARGET RAGE-INHIBITORY ACTIVITY

Semantic Scholar · Medicine · 2020

Abstract

Using a multitarget neural network model of RAGE-inhibitory activity, a consensus virtual screening of a library of new condensed benzimidazole derivatives was performed. Compounds with a essential RAGE-inhibitory effect have been found.

Source of record · Semantic Scholar

Similar papers

[Consensus ensemble neural network multitarget model of RAGE inhibitory activity of chemical compounds].P. Vassiliev, A. Spasov, A. Kochetkov et al.2021 · Biomeditsinskaia khimiia · 1 citation In Library
Deep-learning- and pharmacophore-based prediction of RAGE inhibitorsDavid Z Huang, V. Kouznetsova, I. Tsigelny2020 · Physical Biology · 12 citations In Library
Drug design of new sigma-1 antagonists against neuropathic pain: A QSAR study using partial least squares and artificial neural networksLaise P. A. Chiari, Aldineia P. da Silva, Aline A de Oliveira et al.2021 · Journal of Molecular Structure · 7 citations In Library
Prediction and optimization of NaV1.7 inhibitors based on machine learning methodsWeikaixin Kong, Xinyu Tu, Zhengwei Xie et al.2019 · arXiv.org · 0 citations In Library
Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular dockingMukuo Wang, Shu-Fang Hou, Yu Wei et al.2021 · PLoS Comput. Biol. · 27 citations In Library
Automated design and optimization of multitarget schizophrenia drug candidates by deep learningXiaoqin Tan, Xiangrui Jiang, Yang He et al.2020 · bioRxiv · 38 citations In Library
Prediction and Optimization of NaV1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated AnnealingWeikaixin Kong, Xinyu Tu, Weiran Huang et al.2020 · Journal of Chemical Information and Modeling · 16 citations In Library
Inhibition activity prediction for a dataset of candidates’ drug by combining fuzzy logic with MLR/ANN QSAR modelsA. Abdolmaleki, Jahan B. Ghasemi2019 · Chemical Biology and Drug Design · 14 citations In Library
Navigating into the Chemical Space of Monoamine Oxidase Inhibitors by Artificial Intelligence and Cheminformatics ApproachSunil Kumar, Aathira S. Nair, Vaishnav Bhashkar et al.2021 · ACS Omega · 21 citations In Library
Design, synthesize and antiurease activity of novel thiazole derivatives: Machine learning, molecular docking and biological investigationArif Mermer2020 · Journal of Molecular Structure · 9 citations In Library
© 2026 NYSGPT2525 LLC