H
Horacio Pérez-Sánchez
Researcher at Universidad Católica San Antonio de Murcia
Publications - 193
Citations - 3415
Horacio Pérez-Sánchez is an academic researcher from Universidad Católica San Antonio de Murcia. The author has contributed to research in topics: Virtual screening & Chemistry. The author has an hindex of 25, co-authored 167 publications receiving 2416 citations. Previous affiliations of Horacio Pérez-Sánchez include University of Ioannina & University of Murcia.
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Journal ArticleDOI
MCC950 closes the active conformation of NLRP3 to an inactive state.
Ana Tapia-Abellán,Diego Angosto-Bazarra,Helios Martínez-Banaclocha,Carlos de Torre-Minguela,José P. Cerón-Carrasco,Horacio Pérez-Sánchez,Juan I. Aróstegui,Pablo Pelegrín +7 more
TL;DR: MCC950, a small-molecule inhibitor of the NLRP3 inflammasome, inactivatesNLRP3, including hyperactive disease-linked mutations, by closing the ‘open’ conformation, thereby preventing conformational changes required for NLRP2 activation.
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High-Throughput parallel blind Virtual Screening using BINDSURF.
TL;DR: BINDSURF is an efficient and fast blind methodology for the determination of protein binding sites depending on the ligand, that uses the massively parallel architecture of GPUs for fast pre-screening of large ligand databases.
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Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.
TL;DR: The advantages and disadvantages of the proposed NN algorithms, especially the innovative DL techniques used in ligand-based virtual screening (VS) are discussed.
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Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives.
TL;DR: A clear awareness of present high performance computing (HPC) solutions in bioinformatics, Big Data analysis paradigms for computational biology, and the issues that are still open in the biomedical and healthcare fields represent the starting point to win this challenge.
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Automatic selection of molecular descriptors using random forest
Gaspar Cano,Jose Garcia-Rodriguez,Alberto Garcia-Garcia,Horacio Pérez-Sánchez,Jn Atli Benediktsson,Anil Thapa,Alastair J. Barr +6 more
TL;DR: This work examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes and outperforms classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches.