Publikationen, an denen er mitarbeitet Francisco Prado Prado (21)

2017

  1. Review of theoretical models to study natural products with antiprotozoal activity

    Current Drug Targets, Vol. 18, Núm. 5, pp. 605-616

2016

  1. Model for classification of a ChEMBL dataset of anti-breast cancer-compounds conforming to multiple pharmacological parameters, biological assays, and receptors

    Letters in Drug Design and Discovery, Vol. 13, pp. 491-496

2014

  1. Model for high-throughput screening of drug immunotoxicity - Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry

    European Journal of Medicinal Chemistry, Vol. 72, pp. 206-220

  2. Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates

    International Journal of Molecular Sciences, Vol. 15, Núm. 9, pp. 17035-17064

2013

  1. Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity

    Current Topics in Medicinal Chemistry, Vol. 13, Núm. 14, pp. 1636-1649

  2. Patents of bio-active compounds based on computer-aided drug discovery techniques

    Frontiers in Bioscience - Elite, Vol. 5 E, Núm. 2, pp. 399-407

  3. Review of bioinformatics and theoretical studies of acetylcholinesterase inhibitors

    Current Bioinformatics, Vol. 8, Núm. 4, pp. 496-510

2012

  1. 3D MI-DRAGON: New model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE

    Current Topics in Medicinal Chemistry, Vol. 12, Núm. 16, pp. 1843-1865

  2. ANN multiplexing model of drugs effect on macrophages; Theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen

    Bioorganic and Medicinal Chemistry, Vol. 20, Núm. 20, pp. 6181-6194

  3. QSAR models for computer-aided drug design and molecular docking for disorders of the central nervous system and other diseases

    Current Topics in Medicinal Chemistry

  4. Review of synthesis, assay, and prediction of β and γ-secretase inhibitors

    Current Topics in Medicinal Chemistry, Vol. 12, Núm. 8, pp. 828-844

2011

  1. 2D MI-DRAGON: A new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins

    European Journal of Medicinal Chemistry, Vol. 46, Núm. 12, pp. 5838-5851

  2. Entropy multi-target QSAR model for prediction of antiviral drug complex networks

    Chemometrics and Intelligent Laboratory Systems, Vol. 107, Núm. 2, pp. 227-233

  3. MIND-BEST: Web server for drugs and target discovery; Design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from trichomonas gallinae

    Journal of Proteome Research, Vol. 10, Núm. 4, pp. 1698-1718

  4. NL MIND-BEST: A web server for ligands and proteins discovery-Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum

    Journal of Theoretical Biology, Vol. 276, Núm. 1, pp. 229-249

  5. NL mind-best: aweb server for ligands and proteins discovery; theoretic experimental study of proteins of giardia lamblia

    Proteómica: revista de la Sociedad Española de Proteómica, Núm. 7, pp. 170-170

  6. Review of Bioinformatics and QSAR studies of β-secretase inhibitors

    Current Bioinformatics, Vol. 6, Núm. 1, pp. 3-15

  7. Review of synthesis, biological assay and QSAR studies of β-secretase inhibitors

    Current Computer-Aided Drug Design, Vol. 7, Núm. 4, pp. 263-275

  8. Theoretical study of GSK-3α: Neural networks QSAR studies for the design of new inhibitors using 2D descriptors

    Molecular Diversity, Vol. 15, Núm. 4, pp. 947-955

  9. Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

    European Journal of Medicinal Chemistry, Vol. 46, Núm. 4, pp. 1074-1094