Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction

Klon, Anthony E.; Lowrie, Jeffrey F.; Diller, David J.

doi:10.1021/ci0601315

Cited by 104 publications

(55 citation statements)

References 32 publications

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“…In addition, Bayesian classification methods have also been used for ADME/Tox models (36, 47–49). Thus, using Bayesian models for hit follow-up outside of Mtb is worthy of further exploration.…”

Section: Discussionmentioning

confidence: 99%

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery

et al. 2013

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Purpose Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. Methods A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. Results In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. Conclusion Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.

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Section: Discussionmentioning

confidence: 99%

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery

et al. 2013

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“…While we have focused on Bayesian machine learning due to their processing speed and ease of use, many other algorithms exist that can be used for machine learning. SVM 43–52 and Random Forests 53–55 like Bayesian classification methods 56–60 have also been used extensively for drug discovery and ADME/Tox models 31, 57, 61, 62 . For example, extensive evaluations of different machine learning methods and descriptors have been performed by Broccatelli et al .…”

Section: Discussionmentioning

confidence: 99%

Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation

Ekins

Freundlich

Reynolds

2013

J. Chem. Inf. Model.

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The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multi-drug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external dataset. A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual datasets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three datasets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest five-fold cross validation ROC scores can outperform other models in a test set dependent manner. We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Dataset fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date.

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“…Bayesian models were generated using Discovery Studio 2.1 (Accelrys, San Diego, CA) Laplacian-corrected Bayesian classifier [37],[42],[43],[45],[56]. FCFP_6 fingerprints, AlogP, molecular weight, number of rotatable bonds, number of rings, number of aromatic rings, number of hydrogen bond acceptors, number of hydrogen bond donors and molecular fractional polar surface area were calculated from the input sdf file using the “calculate molecular properties protocol”.…”

Section: Methodsmentioning

confidence: 99%

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

et al. 2009

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Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

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Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction

Cited by 104 publications

References 32 publications

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery

Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

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