A confidence predictor for logD using conformal regression and a support-vector machine

Lapins, Maris; Arvidsson, Staffan; Lampa, Samuel; Berg, Arvid; Schaal, Wesley; Alvarsson, Jonathan; Spjuth, Ola

doi:10.1186/s13321-018-0271-1

Cited by 44 publications

(56 citation statements)

References 37 publications

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“…An advantage of using unhashed fingerprints is that the features have a particular substructure assigned to them and can therefore be traced back to the actual molecular features. Thus using unhashed fingerprints mean that each feature represents a certain molecular substructure, and by assessing feature importance, this can be helpful in interpreting model results in a chemical context [40–42].…”

Section: Resultsmentioning

confidence: 99%

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

et al. 2018

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Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints (), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint’s radius.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0304-9) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

et al. 2018

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“…The descriptors characterized the physicochemical, two-dimensional substructures, and drug-like properties of the studied compounds. Lapins et al ( 2018 ) constructed a QSAR model to predict the lipophilicity of compounds by using a signature molecular descriptor, which is related to the molecular two-dimensional topology information from 1.6 million compounds. Xu et al ( 2017 ) developed three deep learning-based QSAR models to evaluate the acute oral toxicity (AOT) of compounds.…”

Section: In Silico Approachesmentioning

confidence: 99%

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Zhou

et al. 2020

Front. Chem.

208

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Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

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“…In this study we used the Mondrian ACP implementation in the software CPSign (Arvidsson, 2016 ), leveraging the LIBLINEAR SVM implementation (Fan et al, 2008 ) together with the signatures molecular descriptor (Faulon et al, 2003 ). This descriptor is based on the neighboring of atoms in a molecule and has been shown to work well for QSAR studies (Alvarsson et al, 2016 ; Lapins et al, 2018 ) and for ligand-based target prediction (Alvarsson et al, 2014 ). Signatures were generated with height 1-3, which means that molecular sub-graphs including all atoms of distance 1, 2, or 3 from initial atoms, are generated.…”

Section: Methodsmentioning

confidence: 99%

Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction

et al. 2018

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Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only provide point predictions. We here describe a methodology that uses Conformal Prediction for predicting off-target interactions, with models trained on data from 31 targets in the ExCAPE-DB dataset selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence p-values for each class. The full pre-processing and model training process is openly available as scientific workflows on GitHub, rendering it fully reproducible. We illustrate the usefulness of the developed methodology on a set of compounds extracted from DrugBank. The resulting models are published online and are available via a graphical web interface and an OpenAPI interface for programmatic access.

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A confidence predictor for logD using conformal regression and a support-vector machine

Cited by 44 publications

References 37 publications

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction

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