In silico structure‐activity‐relationship (SAR) models from machine learning: a review

Ning, Xia; Karypis, George

doi:10.1002/ddr.20410

Cited by 12 publications

(5 citation statements)

References 92 publications

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“…In the latter case, descriptors for ligands and targets are combined in the feature spaces induced by kernels. In some publications the multitarget learning is erroneously associated with MTL, − although in reality this is an STL model for complex chemical objects, i.e., protein–ligand pairs. In our view, the term MTL should designate only the tasks based on common internal representation, which cannot be reduced to STL.…”

Section: Current Trends In Qsar Methodologymentioning

confidence: 99%

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

et al. 2014

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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.

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Section: Current Trends In Qsar Methodologymentioning

confidence: 99%

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

et al. 2014

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“…So as to improve fitting standards in mathematical model (A Dobchev et al. 2014 ; Ning and Karypis 2011 ), a random forest algorithm was utilized (Fig. 4 ).…”

Section: Machine Learning Methods To Drug Discoverymentioning

confidence: 99%

“…At that point when descriptors are chosen, it is necessary to establish the best mathematical model for correct fitting in structure-activity correlation. So as to improve fitting standards in mathematical model (A Dobchev et al 2014;Ning and Karypis 2011), a random forest algorithm was utilized (Fig. 4).…”

Section: Random Forestmentioning

confidence: 99%

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Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

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“…The Tanimoto coefficient or Euclidian distances in descriptor space [Willett et al, 1998] are often used to derive similarity relationships. Models can be trained with machine-learning methods [Ning and Karypis, 2011] if larger sets of compounds are available. The reliability of the predictions depends both on the similarity threshold, on the number and diversity of ligands, and on the selection of the decoys that were included in the training process [Weill and Rognan, 2009;Andersson et al, 2011;Weill, 2011].…”

Section: Compound Profilingmentioning

confidence: 99%

The Role of Chemogenomics in the Pharmaceutical Industry

Bieler

Koeppen

2012

Drug Development Research

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Preclinical Research The goal of chemogenomics is the exploration of all possible interactions between compounds from the chemical space and targets from the biological space. This huge interaction matrix is only sparsely filled with experimental data but can be complemented by predicted values for selected ligand–target pairs. The prediction by computational chemogenomics uses the similarity principle that states that similar compounds have similar properties and that targets with similar binding sites bind similar ligands. Chemogenomics has a number of interesting applications, e.g. the prediction of the bioprofile of drugs including an assessment of possible adverse drug effects or the support of phenotypic screening by target fishing. This review focuses on applications of computational chemogenomics in pharmaceutical research and on the role that it plays in an industrial environment.

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In silico structure‐activity‐relationship (SAR) models from machine learning: a review

Cited by 12 publications

References 92 publications

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

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

Machine Learning in Drug Discovery: A Review

The Role of Chemogenomics in the Pharmaceutical Industry

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