QSAR Modeling: Where have you been? Where are you going to?

By Artem Cherkasov, Eugene N. Muratov, Denis Fourches, Alexandre Varnek, Igor I. Baskin, M. Cronin, John C. Dearden, Paola Gramatica, Y. Martin, Roberto Todeschini and 10 more. 2014

1,811 citations recorded by the source corpus.

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Pharmacology

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Citations in the corpus

Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017, background): “…the small and fragmented data landscape in chemistry. is achieved by leveraging rule-based knowledge obtained from prior feature engineering research in chemistry [5] to perform weak supervised learning, and combining it with transfer learning methods used in modern deep learning research [25].”
Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions (2015, positive): “…for small molecules have frequently been used in quantitative structure-activity/property relationship (QSAR/QSPR), virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes (Cao et al., 2011; Cherkasov et al., 2014; Gola et al., 2006, Willett, 2014).”
A renaissance of neural networks in drug discovery (2016, background): “Representation learning provided by deep multilayer ANNs will play an increasingly important role in computational drug discovery [97 – 99].”
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017, methodology): “…rule-based methods, modern in-silico modeling in chemistry is therefore predicated on correlating engineered features with the activity or property of the chemical, which is formally known as the field of Quantitative Structure-Activity or Structure-Property Relationship (QSAR/QSPR) modeling [4].”
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, methodology): “…in the ﬁeld include eﬀorts in chemical data curation (Williams et al., 2012), automation of QSAR model building (Cox et al., 2013), exploration of alternative descriptors (Cherkasov et al., 2014), and eﬀorts to help deﬁne the Applicability Domain (AD) of a given QSAR model (Sahigara et al., 2012).”
Computational approaches streamlining drug discovery (2023, methodology): “ML is also implemented in many quantitative SAR (QSAR) algorithms 75 , in which the training set and the resulting models are focused on a given target and a chemical scaffold, helping to guide lead affinity and potency optimization.”
QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery (2018, methodology): “…modeling has grown, diversiﬁed, and evolved to the modeling and virtual screening (VS) of very large data sets comprising thousands of diverse chemical structures and using a wide variety of machine learning techniques (Cherkasov et al., 2014; Mitchell, 2014; Ekins et al., 2015; Goh et al., 2017).”