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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/139079
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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 |
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2019-09 |
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