Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets

Cortés-Ciriano, Isidro

doi:10.1186/s13321-016-0125-7

Cited by 29 publications

(21 citation statements)

References 29 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning models are possibly the most common AI approach used in drug development [2][3][4] . The tasks where machine learning has enabled data-driven decision making, and contributed to unravel fundamental biological aspects of pharmacology, include the prediction of drugs' side effects, computational ADMET profiling, toxicity prediction, the derivation of structural alerts, as well as target-based and ligand-based virtual screening [5][6][7][8][9][10][11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

Cortés-Ciriano

Firth

Bender

et al. 2018

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Chemical information and bioactivity data on a target of interest can be integrated using Quantitative Structure-Activity Relationship (QSAR) 12,36,37 . QSAR embraces those mathematical approaches that regress compound activity on compound descriptors.…”

Section: Introductionmentioning

confidence: 99%

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

Cortés-Ciriano

Firth

Bender

et al. 2018

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…The selection of fingerprints may affect the final results of the identified SAs. Fingerprints such as Morgan, used by Bioalerts (Cortes-Ciriano, 2016 ) might lead to redundant SAs which are very similar and related to the same mechanism.…”

Section: Methods For Detecting Structural Alertsmentioning

confidence: 99%

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

et al. 2018

View full text Add to dashboard Cite

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

show abstract

“…cannot provide a facile and intuitive mean to highlight the fingerprint bit “1” in the context of the 3D structure and also cannot inform us the importance of each bit. But it is worth mentioning that Bioalerts program (Cortes-Ciriano, 2016 ), which is developed based on the RDKit, can offer a very useful function to generate the 2D structure image highlighting with one ECFP bit. Nevertheless, Bioalerts doesn't have a 3D graphic frontend to support the interactive visualization.…”

Section: Methodsmentioning

confidence: 99%

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

et al. 2018

View full text Add to dashboard Cite

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

show abstract

Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets

Cited by 29 publications

References 29 publications

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

Contact Info

Product

Resources

About