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Nematocidal Activities of Protonated Benzimidazolyl Chalcone Using Quantitative Structure-Activity Relationship

• Authors : Guy-Richard, Kone, Mamadou; Stéphane, Dembele, Georges; Bafétigué, Ouattara ...

Subjects: Benzimidazolyl-Chalcone (BZC); Nematocidal Activity; Quantitative Structure-Activity Relationship (QSAR)

• Source: ISSN: 2165-3445.

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Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides

• Authors : Yin, Kedong; Li, Ruifang; Zhang, Shaojie ...

• Source: Advanced Science ; ISSN 2198-3844 2198-3844

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Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment

• Authors : Jiajia Wei; Lei Tian; Fan Nie

Subjects: Petroleum hydrocarbons; Dietary exposure; Predicted No-effect concentrations

• Source: Heliyon, Vol 10, Iss 5, Pp e26808- (2024)

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Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure–activity relationship models

• Authors : Lukman K. Akinola; Adamu Uzairu; Gideon A. Shallangwa

Subjects: Autocorrelation descriptor; Binary logistic regression; Estrogen receptor

• Source: Current Research in Toxicology, Vol 6, Iss , Pp 100158- (2024)

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Potential Therapeutic Target Protein Tyrosine Phosphatase-1B for Modulation of Insulin Resistance with Polyphenols and Its Quantitative Structure–Activity Relationship

• Authors : Rath, P.; Ranjan, A.; Ghosh, A. ...

Subjects: QSAR; Polyphenols; Molecular dynamic simulation

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Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis

• Authors : Ramapraba, Palayanoor Seethapathy; Babu, Bellam Ravindra; Paul, Nallathampi Rajamani Rejin

Subjects: Chemoinformatics; Cloud computing; Drug discovery

• Source: International Journal of Electrical and Computer Engineering (IJECE); Vol 15, No 1: February 2025; 1132-1141 ; 2722-2578 ; 2088-8708 ; 10.11591/ijece.v15i1

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A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC 50 Inhibition Values of FLT3 Tyrosine Kinase

• Authors : Jackson J. Alcázar; Ignacio Sánchez; Cristian Merino

Subjects: FLT3 inhibitors; ligand-based drug design; computer-aided drug design

• Source: Pharmaceuticals, Vol 18, Iss 1, p 96 (2025)

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A (Comprehensive) Review of the Application of Quantitative Structure–Activity Relationship (QSAR) in the Prediction of New Compounds with Anti-Breast Cancer Activity

• Authors : Boris Vasilev; Mariyana Atanasova

Subjects: LBDD; QSAR; anti-breast canceragris

• Source: Applied Sciences ; Volume 15 ; Issue 3 ; Pages: 1206

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A (Comprehensive) Review of the Application of Quantitative Structure–Activity Relationship (QSAR) in the Prediction of New Compounds with Anti-Breast Cancer Activity

• Authors : Boris Vasilev; Mariyana Atanasova

Subjects: LBDD; QSAR; anti-breast cancer

• Source: Applied Sciences, Vol 15, Iss 3, p 1206 (2025)

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Quantitative Structure–Activity Relationship (QSAR) Modeling of Chiral CCR2 Antagonists with a Multidimensional Space of Novel Chirality Descriptors

• Authors : Ramanathan Natarajan; Ganapathy S. Natarajan; Subhash C. Basak

Subjects: quantitative chirality structure–activity relationship (QCSAR); QSAR; chirality

• Source: Molecules, Vol 30, Iss 2, p 307 (2025)

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