Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

Zorn, Kimberley M.; Lane, Thomas R.; Russo, Daniel P.; Clark, Alex M.; Makarov, Vadim; Ekins, Sean

doi:10.1021/acs.molpharmaceut.8b01297

Cited by 56 publications

(114 citation statements)

References 81 publications

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“…Machine learning models for chordoma drug discovery. Several recently published studies of compounds screened against chordoma cell lines 20,21 were used to generate Bayesian machine learning models with our Assay Central software 10,12,[22][23][24][25][26][27][28][29] . In one published chordoma study 1097 compounds were screened against 3 chordoma cell lines (U-CH1, U-CH2, MUG-Chor1) and 27 had chordoma selective cytotoxicity 20 and many of these were EGFR inhibitors.…”

Section: Resultsmentioning

confidence: 99%

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Anderson

Havener

Zorn

et al. 2020

Sci Rep

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Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug repurposing strategy represents an attractive approach. Drugs that have already advanced through human clinical safety trials have the potential to be approved more quickly than de novo discovered medicines on new targets. We have taken two strategies to enable this: (1) generated and validated machine learning models of chordoma inhibition and screened compounds of interest in vitro. (2) Tested combinations of approved kinase inhibitors already being individually evaluated for chordoma. Several published studies of compounds screened against chordoma cell lines were used to generate Bayesian Machine learning models which were then used to score compounds selected from the NIH NCATS industry-provided assets. Out of these compounds, the mTOR inhibitor AZD2014, was the most potent against chordoma cell lines (ic 50 0.35 µM U-CH1 and 0.61 µM U-CH2). Several studies have shown the importance of the mtoR signaling pathway in chordoma and suggest it as a promising avenue for targeted therapy. Additionally, two currently FDA approved drugs, afatinib and palbociclib (EGFR and CDK4/6 inhibitors, respectively) demonstrated synergy in vitro (CI 50 = 0.43) while AZD2014 and afatanib also showed synergy (ci 50 = 0.41) against a chordoma cell in vitro. These findings may be of interest clinically, and this in vitro-and in silico approach could also be applied to other rare cancers. Chordoma is a rare cancer that occurs in the bones of the skull base and spine which is part of a larger class of tumors known as sarcomas. Chordoma tumors develop from cells of the notochord, an embryonic structure that facilitates development of the spine 1. The notochord disappears when the fetus is about 8 weeks old, but some notochord cells remain in the bones of the spine and skull base 2. This is a rare occurrence, but when they do, these cells can turn into chordoma. A chordoma tumor usually grows slowly without symptoms for years before diagnosis, which is often in the 5 th and 6 th decades of life (although it can occur at any age). Studies have demonstrated that skull base chordomas are observed more often in children, whilst spinal chordomas are more frequently observed later in life 2,3. It has also been described that when chordomas metastasize they frequently distribute to the lungs, liver, bones, or lymph nodes. This occurs in 30 to 40 percent of people where the tumor metastasizes to other parts of the body 2. At this point in time there are no known environmental, dietary or lifestyle risk factors for this rare type of cancer. Chordomas often occur at random with no direct inherited genetic trait, however familial cases can be caused by duplications of the brachyury gene 4. A SNP in the brachyury gene occurs in 95 percent of people with this tumor 5,6 , and furthermore, chordomas have ...

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Section: Resultsmentioning

confidence: 99%

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Anderson

Havener

Zorn

et al. 2020

Sci Rep

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“…We have previously described Assay Central TM which uses a Bayesian method, and compared it with other methods against a relatively small number of targets such as drug induced liver injury 50 , estrogen receptor 51 , M. tuberculosis 52 , non-nucleoside reverse transcriptase and whole cell HIV 53 . In general, we found that DNN generally performed the best for five-fold cross validation, but with external test sets this superiority was not observed and generally Assay Central TM performed comparably with SVM classification or DNN.…”

Section: Discussionmentioning

confidence: 99%

“…we have used several of these auto-curated models to predict data that was previously curated and used for training or testing models for HIV whole cell, HIV reverse transcriptase and Mtb inhibition 52,53 . In all cases the ROC for these test sets was > 0.7 ( Figure S4 and S5).…”

Section: Discussionmentioning

confidence: 99%

“…Assay Central TM is proprietary software for curating high-quality datasets, generating Bayesian machine learning models with extended-connectivity fingerprints (ECFP6) generated from the CDK library 56 , and making prospective predictions of potential for bioactivity 1,[51][52][53][57][58][59][60][61][62][63][64] . We have previously described this software in detail 1,[51][52][53][57][58][59][60][61][62][63][64] as well as the interpretation of prediction scores 54,65 , and the metrics generated from internal five-fold cross-validation used to evaluate and compare predictive performances. These metrics include, but are not limited to, ROC score 51 , Cohen's kappa (CK) 66,67 , and Matthew's correlation coefficient (MCC) 68 as described by us previously.…”

Section: Machine Learning -Assay Central Tmmentioning

confidence: 99%

“…To test the predictive performance of auto-curated datasets on larger external test sets, we utilized data from our previous publications for HIV whole cell and reverse transcriptase from NIAID 53 and M. tuberculosis whole cell data from literature 52 . These datasets were predicted against auto-curated training models of HIV, whole cell and reverse transcriptase (Table S5, Figure S4) or M. tuberculosis, respectively (Table S6, Figure S5).…”

Section: External Test Setsmentioning

confidence: 99%

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A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery

Lane¹,

Foil²,

Minerali³

et al. 2020

Preprint

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Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies we have applied multiple machine learning algorithms, modeling metrics and in some cases compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and comparison of our proprietary software Assay CentralTM with random forest, k-Nearest Neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (3 levels). Model performance <a>was</a> assessed using an array of five-fold cross-validation metrics including area-under-the-curve, F1 score, Cohen’s kappa and Matthews correlation coefficient. <a>Based on ranked normalized scores for the metrics or datasets all methods appeared comparable while the distance from the top indicated Assay CentralTM and support vector classification were comparable. </a>Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case where minimal tuning was performed of any of the methods. If anything, Assay CentralTM may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay CentralTMperformance, but support vector classification seems to be a strong competitor. We also apply Assay CentralTM to prospective predictions for PXR and hERG to further validate these models. This work currently appears to be the largest comparison of machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors and algorithms, as well as further refining methods for evaluating and comparing models.

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Advances in Multiobjective Optimization for Drug Discovery and Development

Honeycutt

Zorn

Clark³

et al. 2021

Burger's Medicinal Chemistry and Drug Discovery

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Drug discovery requires the simultaneous optimization of many properties such as bioactivity, absorption, distribution, metabolism, excretion, toxicity, and the underlying physicochemical properties. The ability to satisfy many requirements at once is termed multiobjective optimization, and we will discuss the importance of research in this area for drug discovery and development. In particular, we provide examples of Pareto and other optimization methods and how they can be used in drug discovery to make trade‐offs between different predicted or real molecular properties, and we describe advances in software that applies these approaches.

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Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

Cited by 56 publications

References 81 publications

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Synergistic drug combinations and machine learning for drug repurposing in chordoma

A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery

Advances in Multiobjective Optimization for Drug Discovery and Development

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