A review on machine learning methods for in silico toxicity prediction

Machine learning in chemoinformatics and drug discovery

(2018) Yu-Chen Lo et al.   DRUG DISCOVERY TODAY

The rise of deep learning in drug discovery

(2018) Hongming Chen et al.   DRUG DISCOVERY TODAY

Generative Models for Artificially-intelligent Molecular Design

(2018) Gisbert Schneider   Molecular Informatics

Computer-aided drug design: time to play with novel chemical matter

(2017) Xavier Barril   Expert Opinion on Drug Discovery

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

(2017) Yuting Xu et al.   Journal of Chemical Information and Modeling

Deep learning for computational chemistry

(2017) Garrett B. Goh et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

(2017) Artur Kadurin et al.   MOLECULAR PHARMACEUTICS

Accurate and fast feature selection workflow for high-dimensional omics data

(2017) Yasset Perez-Riverol et al.   PLoS One

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

(2017) Egon L. Willighagen et al.   Journal of Cheminformatics

Descriptors and their selection methods in QSAR analysis: paradigm for drug design

(2016) Danishuddin et al.   DRUG DISCOVERY TODAY

Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation

(2016) Denis Fourches et al.   Journal of Chemical Information and Modeling

An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling$

(2016) K. Mansouri et al.   SAR AND QSAR IN ENVIRONMENTAL RESEARCH

Applicability domain: towards a more formal definition$

(2016) T. Hanser et al.   SAR AND QSAR IN ENVIRONMENTAL RESEARCH

In silico toxicology: computational methods for the prediction of chemical toxicity

(2016) Arwa B. Raies et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

On a simple approach for determining applicability domain of QSAR models

(2015) Kunal Roy et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Machine-learning approaches in drug discovery: methods and applications

(2015) Antonio Lavecchia   DRUG DISCOVERY TODAY

Deep Learning in Drug Discovery

(2015) Erik Gawehn et al.   Molecular Informatics

Deep learning

(2015) Yann LeCun et al.   NATURE

Prediction of human population responses to toxic compounds by a collaborative competition

(2015) Federica Eduati et al.   NATURE BIOTECHNOLOGY

Computational toxicology, friend or foe?

(2015) Nigel Greene et al.   Toxicology Research

The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

(2015) Takaya Saito et al.   PLoS One

Kernel-PCA data integration with enhanced interpretability

(2014) Ferran Reverter et al.   BMC Systems Biology

Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?

(2014) Maykel Cruz-Monteagudo et al.   DRUG DISCOVERY TODAY

Addressing toxicity risk when designing and selecting compounds in early drug discovery

(2014) Matthew D. Segall et al.   DRUG DISCOVERY TODAY

Choosing Feature Selection and Learning Algorithms in QSAR

(2014) Martin Eklund et al.   Journal of Chemical Information and Modeling

Read-across approaches - misconceptions, promises and challenges ahead

(2014) Grace Patlewicz   ALTEX-Alternatives to Animal Experimentation

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

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

Activity Landscapes, Information Theory, and Structure - Activity Relationships

(2013) Preeti Iyer et al.   Molecular Informatics

EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

(2013) Mikel Galar et al.   PATTERN RECOGNITION

(Q)SAR Modeling and Safety Assessment in Regulatory Review

(2012) N L Kruhlak et al.   CLINICAL PHARMACOLOGY & THERAPEUTICS

Feature Selection Methods in QSAR Studies

(2012) Mohammad Goodarzi et al.   JOURNAL OF AOAC INTERNATIONAL

In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner

(2012) Jing Chen et al.   JOURNAL OF MOLECULAR GRAPHICS & MODELLING

Comparison of Different Approaches to Define the Applicability Domain of QSAR Models

(2012) Faizan Sahigara et al.   MOLECULES

Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI

(2012) Noel M O’Boyle   Journal of Cheminformatics

Open Babel: An open chemical toolbox

(2011) Noel M O'Boyle et al.   Journal of Cheminformatics

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints

(2010) Chun Wei Yap   JOURNAL OF COMPUTATIONAL CHEMISTRY

Tautomerism in large databases

(2010) Markus Sitzmann et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods

(2010) Jianxin Duan et al.   JOURNAL OF MOLECULAR GRAPHICS & MODELLING

Computational Toxicology as Implemented by the U.S. EPA: Providing High Throughput Decision Support Tools for Screening and Assessing Chemical Exposure, Hazard and Risk

(2010) Robert Kavlock et al.   JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH-PART B-CRITICAL REVIEWS

Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse

(2010) Barun Bhhatarai et al.   MOLECULAR DIVERSITY

Best Practices for QSAR Model Development, Validation, and Exploitation

(2010) Alexander Tropsha   Molecular Informatics

RUSBoost: A Hybrid Approach to Alleviating Class Imbalance

(2009) Chris Seiffert et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS

Let’s not forget tautomers

(2009) Yvonne Connolly Martin   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

PubChem: a public information system for analyzing bioactivities of small molecules

(2009) Y. Wang et al.   NUCLEIC ACIDS RESEARCH

Prediction of HIV-1 Protease Inhibitors Using Machine Learning Approaches

(2009) Hanbing Rao et al.   Quantitative structure-activity relationships & combinatorial science

What is principal component analysis?

(2008) Markus Ringnér   NATURE BIOTECHNOLOGY

Are the Chemical Structures in Your QSAR Correct?

(2008) Douglas Young et al.   Quantitative structure-activity relationships & combinatorial science

AUC: a misleading measure of the performance of predictive distribution models

(2007) Jorge M. Lobo et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY