scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2017 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

scholarly journals Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

ACS Omega ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 2353-2361 ◽  
Author(s):  
Manu Anantpadma ◽  
Thomas Lane ◽  
Kimberley M. Zorn ◽  
Mary A. Lingerfelt ◽  
Alex M. Clark ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ebola Virus ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

2018 ◽  
Author(s):  
Noé Sturm ◽  
Jiangming Sun ◽  
Yves Vandriessche ◽  
Andreas Mayr ◽  
Günter Klambauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Scaffold Hopping ◽  
Learning Models ◽  
Industrial Data ◽  
Structural Descriptors ◽  
Bioactivity Profile ◽  
Machine Learning Models

<div>This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. </div><div>The fingerprint was used to build machine learning models (multi-task deep learning + SVM) for compound activity predictions towards a panel of 131 targets. </div><div>Quality of the predictions and the scaffold hopping potential of the HTSFP were systematically compared to traditional structural descriptors ECFP. </div><div><br></div>

scholarly journals Multivariate Classification of Drugs using Parametric and Nonparametric Machine Learning Models

2020 ◽  
Vol 9 (3) ◽  
pp. 2021-2027
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Biological Activities ◽  
Biological Effects ◽  
Recursive Feature Elimination ◽  
Drug Candidate ◽  
Learning Models ◽  
Machine Learning Models ◽  
Non Parametric

In pharmaceutical research, traditional drug discovery process is time consuming and expensive, where several compounds are experimentally tested for their biological activities. Series of lab experiments are conducted to analyze newly synthesized drug’s pharmaceutical activities and its biological effects on human. With every new drug discovery, the required clinical properties can be determined using machine learning models and this greatly reduces the experimental cost. This paper explores parametric and non-parametric machine learning models to classify administration properties of drugs and its toxicity. The multinomial classification of drugs was based on their physicochemical and ADMET properties. Balanced data samples were drawn from chEMBL and was pre-processed. Features were reduced using Recursive Feature Elimination and the attributes were ranked based on their importance to reduce highly correlated attributes. The performance of parametric and non-parametric machine learning models was analyzed on cheminformatic data that includes physiochemical, biological and pharmaceutical properties of the drug molecules. Selecting the potent drug candidate along with its administration properties greatly reduces wet lab experimental time and cost. Multiclass classification can be determined efficiently using non-parametric machine learning model. Optimal feature engineering, tuning hyperparameters and adopting hybrid algorithms would result in more accurate predictions in future for cheminformatics data.

scholarly journals Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laura-Jayne Gardiner ◽  
Anna Paola Carrieri ◽  
Jenny Wilshaw ◽  
Stephen Checkley ◽  
Edward O. Pyzer-Knapp ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transcriptome Analysis ◽  
Drug Toxicity ◽  
Learning Models ◽  
Machine Learning Models

A systematic exploration of ΔΔG cutoff ranges in machine learning models for protein mutation stability prediction

2018 ◽  
Vol 16 (05) ◽  
pp. 1840022 ◽  
Author(s):  
Richard Olney ◽  
Aaron Tuor ◽  
Filip Jagodzinski ◽  
Brian Hutchinson
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Substitutions ◽  
Learning Models ◽  
Systematic Exploration ◽  
Wide Range ◽  
The Stability ◽  
Experimental Values ◽  
Machine Learning Models

Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies are time and cost prohibitive. Computational approaches for informing experimentalists where to allocate wet-lab resources are available, including a variety of machine learning models. Assessing the accuracy of machine learning models for predicting the effects of mutations is dependent on experiments for amino acid substitutions performed in vitro. When similar experiments on physical proteins have been performed by multiple laboratories, the use of the data near the juncture of stabilizing and destabilizing mutations is questionable. In this work, we explore a systematic and principled alternative to discarding experimental data close to the juncture of stabilizing and destabilizing mutations. We model the inconclusive range of experimental [Formula: see text] values via 3- and 5-way classifiers, and systematically explore potential boundaries for the range of inconclusive experimental values. We demonstrate the effectiveness of potential boundaries through confusion matrices and heat map visualizations. We explore two novel metrics for assessing viable cutoff ranges, and find that under these metrics, a lower cutoff near [Formula: see text] and an upper cutoff near [Formula: see text] are optimal across multiple machine learning models.

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