Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Machine learning models for drug–target interactions: current knowledge and future directions

(2020) Sofia D’Souza et al.   DRUG DISCOVERY TODAY

MHF-Net: An Interpretable Deep Network for Multispectral and Hyperspectral Image Fusion

(2020) Qi Xie et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

FungiPAD: A Free Web Tool for Compound Property Evaluation and Fungicide-Likeness Analysis

(2019) Meng-yao Wang et al.   JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery

(2019) Zi-Yi Yang et al.   Journal of Chemical Information and Modeling

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health

(2019) SungKu Heo et al.   ENVIRONMENTAL POLLUTION

ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches

(2019) Zhenxing Wu et al.   Journal of Chemical Information and Modeling

Deep neural network in QSAR studies using deep belief network

(2018) Fahimeh Ghasemi et al.   APPLIED SOFT COMPUTING

Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks

(2018) Fahimeh Ghasemi et al.   DRUG DISCOVERY TODAY

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network

(2018) Xiang Li et al.   MOLECULAR PHARMACEUTICS

PADFrag: A Database Built for the Exploration of Bioactive Fragment Space for Drug Discovery

(2018) Jing-Fang Yang et al.   Journal of Chemical Information and Modeling

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates

(2018) Yi Xiong et al.   CURRENT DRUG METABOLISM

Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints

(2018) Geven Piir et al.   ENVIRONMENTAL HEALTH PERSPECTIVES

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets

(2017) Richard L. Marchese Robinson et al.   Journal of Chemical Information and Modeling

Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

(2016) Robert P. Sheridan et al.   Journal of Chemical Information and Modeling

A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology

(2016) Paola Gramatica et al.   Journal of Chemical Information and Modeling

In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines

(2015) Dong-Sheng Cao et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Building Predictive Models inRUsing thecaretPackage

(2015) Max Kuhn   Journal of Statistical Software

QSAR Modeling: Where Have You Been? Where Are You Going To?

(2013) Artem Cherkasov et al.   JOURNAL OF MEDICINAL CHEMISTRY

Drug design for ever, from hype to hope

(2012) G. Seddon et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Best Practices for QSAR Model Development, Validation, and Exploitation

(2010) Alexander Tropsha   Molecular Informatics

Medicinal Chemistry and the Molecular Operating Environment (MOE): Application of QSAR and Molecular Docking to Drug Discovery

(2008) Santiago Vilar et al.   CURRENT TOPICS IN MEDICINAL CHEMISTRY

Prediction of PAH mutagenicity in human cells by QSAR classification1

(2008) E. Papa et al.   SAR AND QSAR IN ENVIRONMENTAL RESEARCH