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QN-Docking: An innovative molecular docking methodology based on Q-Networks

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QN-Docking: An innovative molecular docking methodology based on Q-Networks

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dc.contributor.author Serrano, Antonio es_ES
dc.contributor.author Imbernón, Baldomero es_ES
dc.contributor.author Pérez-Sánchez, Horacio es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.contributor.author Bueno-Crespo, Andrés es_ES
dc.contributor.author Abellán, José L. es_ES
dc.date.accessioned 2021-05-14T03:31:50Z
dc.date.available 2021-05-14T03:31:50Z
dc.date.issued 2020-11 es_ES
dc.identifier.issn 1568-4946 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166344
dc.description.abstract [EN] Molecular docking is often used in computational chemistry to accelerate drug discovery at early stages. Many molecular simulations are performed to select the right pharmacological candidate. However, traditional docking methods are based on optimization heuristics such as Monte Carlo or genetic that try several hundreds of these candidates giving rise to expensive computations. Thus, an alternative methodology called QN-Docking is proposed for developing docking simulations more efficiently. This new approach is built upon Q-learning using a single-layer feedforward neural network to train a single ligand or drug candidate (the agent) to find its optimal interaction with the host molecule. In addition, the corresponding Reinforcement Learning environment and the reward function based on a force-field scoring function are implemented. The proposed method is evaluated in an exemplary molecular scenario based on the kaempferol and beta-cyclodextrin. Results for the prediction phase show that QN-Docking achieves 8x speedup compared to stochastic methods such as METADOCK 2, a novel high-throughput parallel metaheuristic software for docking. Moreover, these results could be extended to many other ligand-host pairs to ultimately develop a general and faster docking method. es_ES
dc.description.sponsorship This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia (Spain) under Projects 20813/PI/18, 20988/PI/18 and 20524/PDC/18, and by the Spanish Ministry of Science and Innovation under grants RTI2018-096384-B-I00, RYC2018-025580-I and CTQ2017-87974-R. The authors also thankfully acknowledge the e-infrastructure program of the Research Council of Norway, and the supercomputer center of UiT -the Arctic University of Norway. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia/20813/PI/18 es_ES
dc.relation Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia/20524/PDC/18 es_ES
dc.relation Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia/20988/PI/18 es_ES
dc.relation MEC/CTQ2017-87974-R es_ES
dc.relation info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ es_ES
dc.relation AGENCIA ESTATAL DE INVESTIGACION/RYC-2018-025580-I es_ES
dc.relation.ispartof Applied Soft Computing es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Q-network es_ES
dc.subject Reinforcement learning es_ES
dc.subject Artificial neural networks es_ES
dc.subject Structure-based drug design es_ES
dc.subject Molecular docking es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title QN-Docking: An innovative molecular docking methodology based on Q-Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.asoc.2020.106678 es_ES
dc.rights.accessRights Embargado es_ES
dc.date.embargoEndDate 2022-09-01 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Serrano, A.; Imbernón, B.; Pérez-Sánchez, H.; Cecilia-Canales, JM.; Bueno-Crespo, A.; Abellán, JL. (2020). QN-Docking: An innovative molecular docking methodology based on Q-Networks. Applied Soft Computing. 96:1-12. https://doi.org/10.1016/j.asoc.2020.106678 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.asoc.2020.106678 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 96 es_ES
dc.relation.pasarela S\425198 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES
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