Naïve Bayesian Models for Vero Cell Cytotoxicity

Perryman, Alexander L.; Patel, Jignesh; Russo, Riccardo; Singleton, Eric; Connell, Nancy; Ekins, Sean; Freundlich, Joel S.

doi:10.1007/s11095-018-2439-9

Cited by 27 publications

(28 citation statements)

References 40 publications

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“…To construct Bayesian models for each training set, Pipeline Pilot 9.5 (BIOVIA, Inc.) was utilized with the nine standard (default) descriptors (i.e., molecular fractional polar surface area, molecular weight, number of hydrogen bond donors, number of hydrogen bond acceptors, ALogP, number of aromatic rings, total number of rings, number of rotatable bonds, and the FCFP_6 substructural fingerprints) that have worked well for us in previous Bayesian models for predicting other properties. 21 , 22 , 31 , 32 The details regarding the different ranges of compounds that were deleted from the training set to generate the different pruned models are in the Supporting Information ( Table S1 ).…”

Section: Resultsmentioning

confidence: 99%

“…This study began with the MLSMR and AZ training sets of 57 824 and 1763 compounds, respectively, and explored fusion of the two data sets and then different levels of pruning the training set, following the precedent of our models for mouse liver microsomal stability 21 and Vero cell cytotoxicity. 31 The end result was a Bayesian model derived from the fusion of the MLSMR and AZ training sets where pruning of both parental data sets was conducted: compounds with a solubility of 25–99 μM were deleted in the AZ set and only the subset of MLSMR compounds with a solubility <25 μM were included. This model predicted subsets of its training set as well or better than the other models, depending on the five-fold cross-validation statistic, and, most importantly, it outperformed the other models in predicting the solubility of two independent external test sets (one from PubChem and one from our laboratory’s compound collection).…”

Section: Discussionmentioning

confidence: 99%

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Pruned Machine Learning Models to Predict Aqueous Solubility

et al. 2020

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Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Pruned Machine Learning Models to Predict Aqueous Solubility

et al. 2020

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“…The THP-1 monocytes were used instead of macrophages due to the possible interference of the differentiating PMA (phorbol myristate acetate) molecule with the LDH reagents. As a result, the presence of this compound could potentially lead to biased results [ 62 , 63 , 64 ]. Figure 3 C,D show cell viability results for the MNPs-RNases on THP-1 and Vero cell lines, respectively.…”

Section: Resultsmentioning

confidence: 99%

Magnetite Nanoparticles Functionalized with RNases against Intracellular Infection of Pseudomonas aeruginosa

et al. 2020

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Current treatments against bacterial infections have severe limitations, mainly due to the emergence of resistance to conventional antibiotics. In the specific case of Pseudomonas aeruginosa strains, they have shown a number of resistance mechanisms to counter most antibiotics. Human secretory RNases from the RNase A superfamily are proteins involved in a wide variety of biological functions, including antimicrobial activity. The objective of this work was to explore the intracellular antimicrobial action of an RNase 3/1 hybrid protein that combines RNase 1 high catalytic and RNase 3 bactericidal activities. To achieve this, we immobilized the RNase 3/1 hybrid on Polyetheramine (PEA)-modified magnetite nanoparticles (MNPs). The obtained nanobioconjugates were tested in macrophage-derived THP-1 cells infected with Pseudomonas aeruginosa PAO1. The obtained results show high antimicrobial activity of the functionalized hybrid protein (MNP-RNase 3/1) against the intracellular growth of P. aeruginosa of the functionalized hybrid protein. Moreover, the immobilization of RNase 3/1 enhances its antimicrobial and cell-penetrating activities without generating any significant cell damage. Considering the observed antibacterial activity, the immobilization of the RNase A superfamily and derived proteins represents an innovative approach for the development of new strategies using nanoparticles to deliver antimicrobials that counteract P. aeruginosa intracellular infection.

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“…Several of our own recent cheminformatics prospective testing efforts have identified compounds active in vitro and in vivo against Chagas disease 13 and the Ebola virus 41 using Bayesian algorithms. This Bayesian approach has also been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability 42 , Caco-2 cell permeability 43 , cytotoxicity 44 and interactions with transporters 45 . We have also used many different machine learning algorithms and descriptors in parallel to identify the optimum combination 46 and address complex problems facing the pharmaceutical industry related to the challenges of improving solubility or metabolic stability 47 while retaining bioactivity.…”

Section: Machine Learning Models In Actionmentioning

confidence: 99%

Exploiting machine learning for end-to-end drug discovery and development

et al. 2019

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A variety of machine learning methods such as Naïve Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger data sets created from high throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting. Learning from history 'Those who do not remember the past are condemned to repeat it' (Santayana). This observation applies as much to drug discovery as it does to other aspects of human endeavor 1. The history of drug discovery is a prelude to the emerging potential of computerassisted data exploration. One constant in drug discovery is that every few years the estimated cost to develop drugs rises further. Less than 20 years ago, developing a drug took ~12 years, cost under a billion dollars, and the biggest challenges were failures due to efficacy or toxicity-induced attrition 2. in vitro pharmacological profiling implemented earlier in the drug discovery process helped to identify some predictable undesirable off-*

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Naïve Bayesian Models for Vero Cell Cytotoxicity

Cited by 27 publications

References 40 publications

Pruned Machine Learning Models to Predict Aqueous Solubility

Pruned Machine Learning Models to Predict Aqueous Solubility

Magnetite Nanoparticles Functionalized with RNases against Intracellular Infection of Pseudomonas aeruginosa

Exploiting machine learning for end-to-end drug discovery and development

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