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Beware of q2!

Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735

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Chemometric Methods in Molecular Design

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Chemometric methods in molecular design

Waterbeemd, H. V. D. (1995) · Medical Entomology and Zoology · Cited by 561

Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection

Golbraikh, A., & Tropsha, A. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1020869118689 · Cited by 504

Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle

Zheng, W., & Tropsha, A. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980033m · Cited by 448

Three-Dimensional Quantitative Similarity−Activity Relationships (3D QSiAR) from SEAL Similarity Matrices

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Comparison of algorithms for dissimilarity-based compound selection

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Quantitative Structure-Antitumor Activity Relationships of Camptothecin Analogues: Cluster Analysis and Genetic Algorithm-Based Studies

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SAR of 9-Amino-1,2,3,4-tetrahydroacridine-Based Acetylcholinesterase Inhibitors: Synthesis, Enzyme Inhibitory Activity, QSAR, and Structure-Based CoMFA of Tacrine Analogues

Recanatini, M., Cavalli, A., Belluti, F., Piazzi, L., Rampa, A., Bisi, A., Gobbi, S., Valenti, P., Andrisano, V., Bartolini, M., et al. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm990971t · Cited by 154

Binding Affinity of Triphenyl Acrylonitriles to Estrogen Receptors: Quantitative Structure-Activity Relationships

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A Linear Model to Predict Chronic Effects of Chemicals on Daphnia magna

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Development of Computer-Aided Molecular Design Methods for Bioengineering Applications

Roughton, B. C. (2013) · Cited by 2

Molecular Modeling Studies of RNA Polymerase II Inhibitors as Potential Anticancer Agents

Agarwal, A., Paliwal, S., & Mishra, R. (2013) · Cited by 2

QSAR Analysis of Anti-Toxoplasma Agents

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The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

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Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model

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QSAR Study of N-((3-Benzamido-4-oxo-3,4-Dihydroquinazolin 2-yl)methyl)-N-(Substituted) Phenyl Benzamide as Antiulcer Agents

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LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based datasetPreprint

Virakarin, P., Saengnil, N., Boonyarit, B., Kinchagawat, J., Laotaew, R., Saeteng, T., Nilsu, T., Suvannang, N., Rungrotmongkol, T., & Nutanong, S. Y. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.24.423424 · Cited by 2

QSAR Modeling and Molecular Docking Studies of New Substituted Pyrazolyl-Pyrimidinones as Potent HIV-1 Inhibitors

Hamdache, B., Tabti, K., Er-rajy, M., Dib, M., ElFarouki, K., Ouchetto, K., Elhallaoui, M., Hafid, A., Khouili, M., & Ouchetto, H. (2024) · Current Chemical Biology · doi:10.2174/0122127968317638241014090751 · Cited by 2

Design and Evaluation of Novel Selective BET BD2 Inhibitors by Combining QSAR, Molecular Docking and Molecular Dynamics Simulation Techniques

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Design and screening of novel 1,2,4-Triazole-3-thione derivatives as DCN1 inhibitors for anticardiac fibrosis based on 3D-QSAR modeling and molecular dynamics

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In silico design of novel dihydropteridone derivatives with oxadiazoles as potent inhibitors of MCF-7 breast cancer cells

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Computer-Aided Discovery of Drugs Against Chagas Disease

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Multivariate analysis about the influence of solubility and partition molecular descriptors on LogKoc for substituted diphenyl ethers

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Structure-activity relationships of taxoids: a molecular descriptors family approach

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Virtual Screening Applications in the Search of Novel Antiepileptic Drug Candidates

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3-phenylquinolinylchalcone derivatives: pharmacophore modelling, 3d-qsar analysis and docking studies as anti-cancer agents

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Predictive Modeling of Molecular Properties: Can We Go beyond Interpretation?

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Therapeutic Potential of N-Type Voltage-Gated Ca2+ Channel

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Predictive 3D‐Quantitative Structure‐Activity Relationship for A1 and A2A Adenosine Receptor Ligands

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Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

Casañola‐Martín, G. M., Khan, M. T. H., Le‐Thi‐Thu, H., Marrero‐Ponce, Y., García‐Domenech, R., Torrens, F., & Rescigno, A. (2012) · International Journal of Chemoinformatics and Chemical Engineering · doi:10.4018/ijcce.2012070104 · Cited by 1

In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport

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QSAR Study of Indolylisoxazoline Analogues for Their Antiprostat Activity

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A simple model for the assessment of the agonistic activity of dibenzazepine derivatives by molecular moieties

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Chemometric Modeling of Algal and Daphnia Toxicity

Crisan, L., Borota, A., Bora, A., Funar‐Timofei, S., & Ilia, G. (2021) · Wiley eBooks · doi:10.1002/9781119681397.ch13 · Cited by 1

Machine Learning Search of Novel Selective NaV1.2 and NaV1.6 Inhibitors as Potential Treatment Against Dravet Syndrome

Fallico, M. J., Alberca, L. N., Gori, D. N. P., Gavernet, L., & Talevi, A. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-08443-0_7 · Cited by 1

Corpus Generation for Multi-class Bangla Email Classification and Its Evaluation

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Prediction of DILI in Humans: Investigating Opportunities in DILIst Data, Chemical Features, Machine Learning and Rule-Based Methods

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Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments

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Experimental insights and modeling innovations: Deciphering Fe(VI) oxidation in imidazole ionic liquids through QSAR and RFR

Li, B., Qu, R., Wang, T., Guo, R., Tian, J. M., Li, S., Abukhadra, M. R., Mahmoud, R., & Wang, Z. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134980 · Cited by 1

Modeling skin sensitization: hierarchical support vector regression−based prediction of lysine depletion in DPRA

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Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Wang, F. (2009) · FIU - Digital Commons (Florida International University) · doi:10.25148/etd.fi10041625 · Cited by 1

Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity predictionPreprint

Wu, K., & Wei, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10951 · Cited by 1

In Silico ADME: QSPR/QSAR

Talevi, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-51519-5_149-1 · Cited by 1

In Silico ADME: QSPR/QSAR

Talevi, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-84860-6_149 · Cited by 1

Improved CoMFA Modeling by Optimization of Settings : Toward the Design of Inhibitors of the HCV NS3 Protease

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Computer Applications in Catalysis Research

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Annotation and classification of chemical space in chemogenomics

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Computer-aided methodologies to predict endocrine-disrupting potency of chemicals

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Combined SVM-PLS Method for Predicting Estrogenic Activities of Organic Chemicals in the Coastal Water

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Cheminformatics Modeling of Diverse and Disparate Biological Data and the Use of Models to Discover Novel Bioactive Molecules

Luo, M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/tkd5-e303

Classifier Design to Improve Pattern Classification and Knowledge Discovery for Imbalanced Datasets

Wang, K. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/g3gq-9f55

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Computational studies of biomolecular screening and interactions

Niinivehmas, S. P. (2015) · Jyväskylä University Digital Archive (University of Jyväskylä)

The rm2 Metrics for Validation of QSAR/QSPR Models

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Modeling adverse liver effects of drugs using kNN QSAR method

Rodgers, A. D. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/cwtv-nn49

The Calculation Method of Safety Degree and Its Application in Coal Mine Enterprises

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Modeling and predicting the solute polarity parameter in reversed‐phase liquid chromatography using quantitative structure–property relationship approaches

Golmohammadi, H., Dashtbozorgi, Z., & Khooshechin, S. (2017) · Journal of Separation Science · doi:10.1002/jssc.201700603

Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

Golmohammadi, H., Dashtbozorgi, Z., & Khooshechin, S. (2017) · Acta Physico-Chimica Sinica · doi:10.3866/pku.whxb201701163

In silico strategies to study polypharmacology of G-protein-coupled receptors

Hajjo, R. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/xtc7-2969

JavaDL: a Java-based Deep Learning Tool to Predict Drug ResponsesPreprint

Huang, B., Fong, L. W. R., Chaudhari, R. R., Tan, Z., & Zhang, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.04.077701

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Permadi, F. (2020) · Majalah Farmasi dan Farmakologi · doi:10.20956/mff.v24i2.10517

DFT Based QSAR Studies of Phenyl Triazolinones of Protoporphyrinogen Oxidase Inhibitors

Sarkar, B. K. (2020) · Asian Journal of Organic & Medicinal Chemistry · doi:10.14233/ajomc.2020.ajomc-p280

Quantitative models for predicting antioxidant capacity in herbs based on molecular structures and compositions

Ahmad, M. M. (2019)

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