Abstract
Virtual Screening (VS) is a technique aimed at reducing the time and budget required when working on drug discovery campaigns. The idea consists of applying computational procedures to prefilter databases to a subset of potential compounds, to be characterized experimentally in later phases.
The problem lies in the fact that the current VS methods make simplifications, meaning they are not exhaustive. One particular common simplification is to consider the molecules as rigid. Such an assumption greatly reduces the computational complexity of the optimization problem to be solved, but it may result in poor or inefficient predictions. In this work, we have extended the features of Optipharm, a recently developed piece of software, by applying a methodology that considers the flexibility of the molecules. The new OptiPharm has several strengths over its previous version. More precisely, (i) it includes a prefilter based on molecule descriptors, (ii) simulates molecule flexibility by computing different poses for each rotatable bond, (iii) reduces the search space dimension, and (iv) introduces circular limits for the angular variables to enhance searchability. As the results show, these improvements help OptiPharm to achieve better predictions.
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Acknowledgement
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 “Ayudas a proyectos de investigación I+D+I en el marco del Programa Operativo FEDER 2014-20” grant (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. et al. (2022). Virtual Screening Based on Electrostatic Similarity and Flexible Ligands. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378. Springer, Cham. https://doi.org/10.1007/978-3-031-10562-3_10
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