Abstract
Recently, a new piece of software called OptiPharm has been proposed to optimize the similarity between two given molecules. A comprehensive study proved it was very competitive compared with state-of-the-art algorithms such as WEGA and ROCS. However, all of these methods, including OptiPharm, assume the proteins as rigid, resulting in poor or inefficient predictions. The consideration of conformational changes and thus the molecule’s flexibility is necessary. In this work, we have extended the OptiPharm’s functionality by applying a methodology that considers the flexibility of the molecules. Apart from that, the new OptiPharm presents some strengths regarding its previous version. More precisely, it reduces the search space dimension and introduces circular limits for the angular variables to enhance searchability. As results will show, these improvements help OptiPharm to achieve better predictions.
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Acknowledgments
This work was supported by the Spanish Ministry of Economy and Competitiveness through the CTQ2017-87974-R, RTI2018-095993-B-I00 and EQC2019-006418-P grants; by the Junta de Andalucía through the grant Proyectos de excelencia (P18-RT-1193), by the Programa Regional de Fomento de la Investigación (Plan de Actuación 2018, Región de Murcia, Spain) through the: ”Ayudas a la realización de proyectos para el desarrollo de investigación científica y técnica por grupos competitivos (20988/PI/18)” grant; by the University of Almeria throught the grant: Ayudas a proyectos de investigación I+D+I en el marco del Programa Operativo FEDER 2014-20” (UAL18-TIC-A020-B). Savíns Puertas Martín is a fellow of the ‘Margarita Salas’ grant (RR_A_2021_21), financed by the European Union (NextGenerationEU).
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Puertas-Martín, S., Redondo, J.L., Garzón, E.M., Pérez-Sánchez, H., Ortigosa, P.M. (2022). Increasing the Accuracy of Optipharm’s Virtual Screening Predictions by Implementing Molecular Flexibility. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_20
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