Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model

Autores
Goicoechea, Sofía; Sbaraglini, María Laura; Chuguransky, Sara Rocío; Morales, Juan Francisco; Ruiz, María Esperanza; Talevi, Alan; Bellera, Carolina Leticia
Año de publicación
2019
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Epilepsy is the second most common chronic brain disorder, affecting 65 million people worldwide. According to the NIH’s Epilepsy Therapy Screening Program, evaluation of potential new antiepileptic drug candidates begins with assessment of their protective effects in two acute seizure models in mice, the Maximal Electroshock Seizure test and the 6 Hz test. The latter elicits partial seizures through an electrical stimulus of 44 mA, at which many clinically established anti-seizure drugs do not suppress seizures. The inclusion of this “high-hurdle” acute seizure assay at the initial stage of the drug identification phase is intended to increase the probability that agents with improved efficacy will be detected. In this work, we have used machine learning approximations to develop in silico models capable of identifying novel anticonvulsant drugs with protective effects in the 6 Hz seizure model. Linear classifiers based on Dragon conformation-independent descriptors were generated through an in-house routine in R environment and validated through standard validation procedures. They were later combined through different ensemble learning schemes. The best ensemble comprised the 29 best-performing models combined using the MIN operator. With the objective of finding new drug repurposing opportunities (i.e. identifying second or further therapeutic indications, in our case anticonvulsant activity, in existing drugs), such model ensemble was applied in a virtual screening campaign of DrugBank and Sweetlead databases. 28 approved drugs were identified as potential protective agents in the 6 Hz model. The present study constitutes an example of the use of machine learning approximations to systematically guide drug repurposing projects.
Laboratorio de Investigación y Desarrollo de Bioactivos
Materia
Química
Machine learning
Ensemble learning
6 Hz seizure model
Anticonvulsant drugs
Virtual screening
Epilepsy
Drug repurposing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/139079

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spelling Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure ModelGoicoechea, SofíaSbaraglini, María LauraChuguransky, Sara RocíoMorales, Juan FranciscoRuiz, María EsperanzaTalevi, AlanBellera, Carolina LeticiaQuímicaMachine learningEnsemble learning6 Hz seizure modelAnticonvulsant drugsVirtual screeningEpilepsyDrug repurposingEpilepsy is the second most common chronic brain disorder, affecting 65 million people worldwide. According to the NIH’s Epilepsy Therapy Screening Program, evaluation of potential new antiepileptic drug candidates begins with assessment of their protective effects in two acute seizure models in mice, the Maximal Electroshock Seizure test and the 6 Hz test. The latter elicits partial seizures through an electrical stimulus of 44 mA, at which many clinically established anti-seizure drugs do not suppress seizures. The inclusion of this “high-hurdle” acute seizure assay at the initial stage of the drug identification phase is intended to increase the probability that agents with improved efficacy will be detected. In this work, we have used machine learning approximations to develop in silico models capable of identifying novel anticonvulsant drugs with protective effects in the 6 Hz seizure model. Linear classifiers based on Dragon conformation-independent descriptors were generated through an in-house routine in R environment and validated through standard validation procedures. They were later combined through different ensemble learning schemes. The best ensemble comprised the 29 best-performing models combined using the MIN operator. With the objective of finding new drug repurposing opportunities (i.e. identifying second or further therapeutic indications, in our case anticonvulsant activity, in existing drugs), such model ensemble was applied in a virtual screening campaign of DrugBank and Sweetlead databases. 28 approved drugs were identified as potential protective agents in the 6 Hz model. The present study constitutes an example of the use of machine learning approximations to systematically guide drug repurposing projects.Laboratorio de Investigación y Desarrollo de Bioactivos2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf3-19http://sedici.unlp.edu.ar/handle/10915/139079spainfo:eu-repo/semantics/altIdentifier/issn/1865-0929info:eu-repo/semantics/altIdentifier/issn/1865-0937info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-36636-0_1info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:31:57Zoai:sedici.unlp.edu.ar:10915/139079Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:57.31SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
title Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
spellingShingle Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
Goicoechea, Sofía
Química
Machine learning
Ensemble learning
6 Hz seizure model
Anticonvulsant drugs
Virtual screening
Epilepsy
Drug repurposing
title_short Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
title_full Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
title_fullStr Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
title_full_unstemmed Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
title_sort Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model
dc.creator.none.fl_str_mv Goicoechea, Sofía
Sbaraglini, María Laura
Chuguransky, Sara Rocío
Morales, Juan Francisco
Ruiz, María Esperanza
Talevi, Alan
Bellera, Carolina Leticia
author Goicoechea, Sofía
author_facet Goicoechea, Sofía
Sbaraglini, María Laura
Chuguransky, Sara Rocío
Morales, Juan Francisco
Ruiz, María Esperanza
Talevi, Alan
Bellera, Carolina Leticia
author_role author
author2 Sbaraglini, María Laura
Chuguransky, Sara Rocío
Morales, Juan Francisco
Ruiz, María Esperanza
Talevi, Alan
Bellera, Carolina Leticia
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Química
Machine learning
Ensemble learning
6 Hz seizure model
Anticonvulsant drugs
Virtual screening
Epilepsy
Drug repurposing
topic Química
Machine learning
Ensemble learning
6 Hz seizure model
Anticonvulsant drugs
Virtual screening
Epilepsy
Drug repurposing
dc.description.none.fl_txt_mv Epilepsy is the second most common chronic brain disorder, affecting 65 million people worldwide. According to the NIH’s Epilepsy Therapy Screening Program, evaluation of potential new antiepileptic drug candidates begins with assessment of their protective effects in two acute seizure models in mice, the Maximal Electroshock Seizure test and the 6 Hz test. The latter elicits partial seizures through an electrical stimulus of 44 mA, at which many clinically established anti-seizure drugs do not suppress seizures. The inclusion of this “high-hurdle” acute seizure assay at the initial stage of the drug identification phase is intended to increase the probability that agents with improved efficacy will be detected. In this work, we have used machine learning approximations to develop in silico models capable of identifying novel anticonvulsant drugs with protective effects in the 6 Hz seizure model. Linear classifiers based on Dragon conformation-independent descriptors were generated through an in-house routine in R environment and validated through standard validation procedures. They were later combined through different ensemble learning schemes. The best ensemble comprised the 29 best-performing models combined using the MIN operator. With the objective of finding new drug repurposing opportunities (i.e. identifying second or further therapeutic indications, in our case anticonvulsant activity, in existing drugs), such model ensemble was applied in a virtual screening campaign of DrugBank and Sweetlead databases. 28 approved drugs were identified as potential protective agents in the 6 Hz model. The present study constitutes an example of the use of machine learning approximations to systematically guide drug repurposing projects.
Laboratorio de Investigación y Desarrollo de Bioactivos
description Epilepsy is the second most common chronic brain disorder, affecting 65 million people worldwide. According to the NIH’s Epilepsy Therapy Screening Program, evaluation of potential new antiepileptic drug candidates begins with assessment of their protective effects in two acute seizure models in mice, the Maximal Electroshock Seizure test and the 6 Hz test. The latter elicits partial seizures through an electrical stimulus of 44 mA, at which many clinically established anti-seizure drugs do not suppress seizures. The inclusion of this “high-hurdle” acute seizure assay at the initial stage of the drug identification phase is intended to increase the probability that agents with improved efficacy will be detected. In this work, we have used machine learning approximations to develop in silico models capable of identifying novel anticonvulsant drugs with protective effects in the 6 Hz seizure model. Linear classifiers based on Dragon conformation-independent descriptors were generated through an in-house routine in R environment and validated through standard validation procedures. They were later combined through different ensemble learning schemes. The best ensemble comprised the 29 best-performing models combined using the MIN operator. With the objective of finding new drug repurposing opportunities (i.e. identifying second or further therapeutic indications, in our case anticonvulsant activity, in existing drugs), such model ensemble was applied in a virtual screening campaign of DrugBank and Sweetlead databases. 28 approved drugs were identified as potential protective agents in the 6 Hz model. The present study constitutes an example of the use of machine learning approximations to systematically guide drug repurposing projects.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
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http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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