OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors
Rahul Singh, Parvin Kumar, Meena Devi, et al.
New Journal of Chemistry (2022) Vol. 46, Iss. 39, pp. 19062-19072
Closed Access | Times Cited: 15
Rahul Singh, Parvin Kumar, Meena Devi, et al.
New Journal of Chemistry (2022) Vol. 46, Iss. 39, pp. 19062-19072
Closed Access | Times Cited: 15
Showing 15 citing articles:
A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers
Shahin Ahmadi, Shahram Lotfi, Hamideh Hamzehali, et al.
RSC Advances (2024) Vol. 14, Iss. 5, pp. 3186-3201
Open Access | Times Cited: 7
Shahin Ahmadi, Shahram Lotfi, Hamideh Hamzehali, et al.
RSC Advances (2024) Vol. 14, Iss. 5, pp. 3186-3201
Open Access | Times Cited: 7
Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies
Rahul Singh, Parvin Kumar, Jayant Sindhu, et al.
Computers in Biology and Medicine (2023) Vol. 157, pp. 106776-106776
Closed Access | Times Cited: 19
Rahul Singh, Parvin Kumar, Jayant Sindhu, et al.
Computers in Biology and Medicine (2023) Vol. 157, pp. 106776-106776
Closed Access | Times Cited: 19
Quasi-SMILES as a basis for the development of QSPR models to predict the CO2 capture capacity of deep eutectic solvents using correlation intensity index and consensus modelling
Parvin Kumar, Ashwani Kumar, Jayant Sindhu, et al.
Fuel (2023) Vol. 345, pp. 128237-128237
Closed Access | Times Cited: 15
Parvin Kumar, Ashwani Kumar, Jayant Sindhu, et al.
Fuel (2023) Vol. 345, pp. 128237-128237
Closed Access | Times Cited: 15
Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique
Shahram Lotfi, Shahin Ahmadi, Ali Azimi, et al.
New Journal of Chemistry (2023) Vol. 47, Iss. 42, pp. 19504-19515
Closed Access | Times Cited: 7
Shahram Lotfi, Shahin Ahmadi, Ali Azimi, et al.
New Journal of Chemistry (2023) Vol. 47, Iss. 42, pp. 19504-19515
Closed Access | Times Cited: 7
Fragments of local symmetry in a sequence of amino acids: Does one can use for QSPR/QSAR of peptides?
Alla P. Toropova, Andrey A. Toropov, Parvin Kumar, et al.
Journal of Molecular Structure (2023) Vol. 1293, pp. 136300-136300
Closed Access | Times Cited: 5
Alla P. Toropova, Andrey A. Toropov, Parvin Kumar, et al.
Journal of Molecular Structure (2023) Vol. 1293, pp. 136300-136300
Closed Access | Times Cited: 5
CORAL: probing the structural requirements for α-amylase inhibition activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives based on QSAR with correlation intensity index, molecular docking, molecular dynamics, and ADMET studies
Rahul Singh, Parvin Kumar, Jayant Sindhu, et al.
Journal of Biomolecular Structure and Dynamics (2023), pp. 1-18
Closed Access | Times Cited: 5
Rahul Singh, Parvin Kumar, Jayant Sindhu, et al.
Journal of Biomolecular Structure and Dynamics (2023), pp. 1-18
Closed Access | Times Cited: 5
Multicellular target QSAR models for predicting of novel inhibitors against pancreatic cancer by Monte Carlo approach
Shahin Ahmadi, Shahram Lotfi, Ali Azimi, et al.
Results in Chemistry (2024) Vol. 10, pp. 101734-101734
Open Access | Times Cited: 1
Shahin Ahmadi, Shahram Lotfi, Ali Azimi, et al.
Results in Chemistry (2024) Vol. 10, pp. 101734-101734
Open Access | Times Cited: 1
Design of Novel Anti-MRSA Inhibitors: A Computational Study Integrating QSAR, ADMET, and Molecular Dynamics Simulation
Hui-Ying Jiang, Jie Xu, Zhonghua Wang, et al.
New Journal of Chemistry (2024)
Closed Access | Times Cited: 1
Hui-Ying Jiang, Jie Xu, Zhonghua Wang, et al.
New Journal of Chemistry (2024)
Closed Access | Times Cited: 1
Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles
Andrey A. Toropov, Matteo Riccardo Di Nicola, Alla P. Toropova, et al.
Chemosphere (2022) Vol. 312, pp. 137224-137224
Closed Access | Times Cited: 7
Andrey A. Toropov, Matteo Riccardo Di Nicola, Alla P. Toropova, et al.
Chemosphere (2022) Vol. 312, pp. 137224-137224
Closed Access | Times Cited: 7
The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, et al.
Toxicology in Vitro (2023) Vol. 91, pp. 105629-105629
Closed Access | Times Cited: 3
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, et al.
Toxicology in Vitro (2023) Vol. 91, pp. 105629-105629
Closed Access | Times Cited: 3
Analysis of good and bad fingerprint for identification of NIR based optical frameworks using Monte Carlo method
Surbhi Goyal, Payal Rani, Monika Chahar, et al.
Microchemical Journal (2023) Vol. 196, pp. 109549-109549
Closed Access | Times Cited: 3
Surbhi Goyal, Payal Rani, Monika Chahar, et al.
Microchemical Journal (2023) Vol. 196, pp. 109549-109549
Closed Access | Times Cited: 3
Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, et al.
Toxicology Mechanisms and Methods (2024) Vol. 34, Iss. 7, pp. 737-742
Open Access
Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, et al.
Toxicology Mechanisms and Methods (2024) Vol. 34, Iss. 7, pp. 737-742
Open Access
CORAL: Predictions of Quality of Rice Based on Retention Index Using a Combination of Correlation Intensity Index and Consensus Modelling
Parvin Kumar, Ashwani Kumar
Challenges and advances in computational chemistry and physics (2023), pp. 421-462
Closed Access | Times Cited: 1
Parvin Kumar, Ashwani Kumar
Challenges and advances in computational chemistry and physics (2023), pp. 421-462
Closed Access | Times Cited: 1
SMILES and Quasi-SMILES in QSAR Modeling for Prediction of Physicochemical and Biochemical Properties
Siyun Yang, Supratik Kar, Jerzy Leszczyński
Challenges and advances in computational chemistry and physics (2023), pp. 327-348
Closed Access
Siyun Yang, Supratik Kar, Jerzy Leszczyński
Challenges and advances in computational chemistry and physics (2023), pp. 327-348
Closed Access
Quantitative structure-property relationship modeling for prediction of chromatography retention indices of volatile organic compounds in peppers based on Monte Carlo approach
Shahin Ahmadi, Shahram Lotfi, Hamideh Hamzehali, et al.
Research Square (Research Square) (2023)
Open Access
Shahin Ahmadi, Shahram Lotfi, Hamideh Hamzehali, et al.
Research Square (Research Square) (2023)
Open Access
