M. Cruz-Monteagudo

发表

Markov Entropy Centrality: Chemical, Biological, Crime, and Legislative Networks

Maykel Cruz-Monteagudo, Alejandro Pazos, Cristian R. Munteanu, 2011, Towards an Information Theory of Complex Networks.

Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

K. Chou, E. Uriarte, C. Munteanu, 2008 .

Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. ocular irritability classification model

H. González-Díaz, M. Cruz-Monteagudo, F. Borges, 2006, Bulletin of mathematical biology.

Simple Stochastic Fingerprints Towards Mathematical Modeling in Biology and Medicine 2. Unifying Markov Model for Drugs Side Effects

E. Uriarte, H. González-Díaz, M. Cruz-Monteagudo, 2006, Bulletin of mathematical biology.

Chemoinformatics profiling of ionic liquids--automatic and chemically interpretable cytotoxicity profiling, virtual screening, and cytotoxicophore identification.

Maykel Cruz-Monteagudo, Miguel Jorge, Evys Ancede-Gallardo, 2013, Toxicological sciences : an official journal of the Society of Toxicology.

QSAR, Complex Networks, Principal Components and Partial Order Analysis of Drug Cardiotoxicity with Proteome Mass- Spectra Topological Indices

C. Munteanu, H. González-Díaz, M. Cruz-Monteagudo, 2012 .

Quantitative Proteome-Property Relationships (QPPRs). Part 1: finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra.

E. Uriarte, C. Munteanu, H. González-Díaz, 2008, Bioorganic & medicinal chemistry.

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy.

Maykel Cruz-Monteagudo, M Natália D S Cordeiro, Humberto González-Díaz, 2008, Chemical research in toxicology.

Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs

E. Uriarte, H. González-Díaz, M. Cruz-Monteagudo, 2007 .

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

M. Cruz-Monteagudo, F. Borges, M. Cordeiro, 2017, BMC Medical Genomics.

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

Reinaldo Molina Ruiz, M. Cruz-Monteagudo, F. Borges, 2008, Journal of combinatorial chemistry.

Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?

Maykel Cruz-Monteagudo, José L Medina-Franco, Orazio Nicolotti, 2014, Drug discovery today.

Multidimensional Drug Design: Simultaneous Analysis of Binding and Relative Efficacy Profiles of N6‐substituted‐4′‐thioadenosines A3 Adenosine Receptor Agonists

M. Cruz-Monteagudo, F. Borges, M. Cordeiro, 2010, Chemical biology & drug design.

Quantitative Structure-Epigenetic Activity Relationships

J. Medina-Franco, M. Cruz-Monteagudo, Mario Omar García-Sánchez, 2017 .

Unified drug-target interaction thermodynamic Markov model using stochastic entropies to predict multiple drugs side effects.

Maykel Cruz-Monteagudo, Humberto González-Díaz, H. González-Díaz, 2005, European journal of medicinal chemistry.

DRUG-TARGET INTERACTION THERMODYNAMIC MARKOV MODEL TO PREDICT MULTIPLE DRUGS SIDE EFFECTS

E. Uriarte, H. González-Díaz, M. Cruz-Monteagudo, 2005 .

QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices.

Maykel Cruz-Monteagudo, Lourdes Santana, Humberto González-Díaz, 2005, Bioorganic & medicinal chemistry letters.

Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold

Aliuska Morales Helguera, M. Cruz-Monteagudo, F. Borges, 2017, Current neuropharmacology.

Unravelling the relationship between protein sequence and low-complexity regions entropies: Interactome implications.

M. Cruz-Monteagudo, E. Tejera, J. Nieto-Villar, 2015, Journal of theoretical biology.

Physico-Chemical and Structural Interpretation of Discrete Derivative Indices on N-Tuples Atoms

Y. Marrero-Ponce, M. Cruz-Monteagudo, S. Barigye, 2016, International journal of molecular sciences.

aided molecular design III : 2 . 5 D indices for the discovery of antibacterials

L. Santana, H. González-Díaz, M. Cruz-Monteagudo, 2005 .

Chemoinformatics profiling of ionic liquids--uncovering structure-cytotoxicity relationships with network-like similarity graphs.

M. Cruz-Monteagudo, M. Cordeiro, 2014, Toxicological sciences : an official journal of the Society of Toxicology.

Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A2AAdenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors.

Maykel Cruz-Monteagudo, Fernanda Borges, Eduardo Tejera, 2016, Current pharmaceutical design.

Application of desirability-based multi(bi)-objective optimization in the design of selective arylpiperazine derivates for the 5-HT1A serotonin receptor.

I Rebelo, E Tejera, M Cruz-Monteagudo, 2009, European journal of medicinal chemistry.

Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery.

J. Medina-Franco, M. Cruz-Monteagudo, E. Tejera, 2017, Drug discovery today.

Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands

M. Natália D. S. Cordeiro, Maykel Cruz-Monteagudo, Eduardo Tejera, 2015, J. Chem. Inf. Model..

Efficient and biologically relevant consensus strategy for Parkinson’s disease gene prioritization

Aliuska Morales Helguera, M. Cruz-Monteagudo, M. Cordeiro, 2016, BMC Medical Genomics.

Computational chemistry approach for the early detection of drug‐induced idiosyncratic liver toxicity

M. Natália D. S. Cordeiro, Maykel Cruz-Monteagudo, Fernanda Borges, 2008, J. Comput. Chem..

Recent advances on QSAR-based profiling of agonist and antagonist A3 adenosine receptor ligands.

Maykel Cruz-Monteagudo, Feng Luan, M Natália D S Cordeiro, 2013, Current topics in medicinal chemistry.

Evolutionary computation and QSAR research.

Maykel Cruz-Monteagudo, Marcos Gestal, Julian Dorado, 2013, Current computer-aided drug design.

Desirability-based multi-objective QSAR in drug discovery.

Maykel Cruz-Monteagudo, Eduardo Tejera, Fernanda Borges, 2012, Mini reviews in medicinal chemistry.

Prioritizing Hits with Appropriate Trade‐Offs Between HIV‐1 Reverse Transcriptase Inhibitory Efficacy and MT4 Blood Cells Toxicity Through Desirability‐Based Multiobjective Optimization and Ranking

Maykel Cruz-Monteagudo, Fernanda Borges, Hai PhamThe, 2010, Molecular informatics.

Computational modeling tools for the design of potent antimalarial bisbenzamidines: overcoming the antimalarial potential of pentamidine.

Maykel Cruz-Monteagudo, Fernanda Borges, M. Cruz-Monteagudo, 2007, Bioorganic & medicinal chemistry.

Generalized Molecular Descriptors Derived From Event-Based Discrete Derivative.

Maykel Cruz-Monteagudo, Francisco Torrens, Yovani Marrero-Ponce, 2016, Current pharmaceutical design.

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

M. Cruz-Monteagudo, M. Cordeiro, E. Tejera, 2017, BMC Medical Genomics.

Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson’s Disease Dual Target Ligands

M. Natália D. S. Cordeiro, Maykel Cruz-Monteagudo, Fernanda Borges, 2017, Current neuropharmacology.

From flamingo dance to (desirable) drug discovery: a nature-inspired approach.

Stephan C Schürer, Maykel Cruz-Monteagudo, Fernanda Borges, 2017, Drug discovery today.

Jointly Handling Potency and Toxicity of Antimicrobial Peptidomimetics by Simple Rules from Desirability Theory and Chemoinformatics

M. Natália D. S. Cordeiro, Maykel Cruz-Monteagudo, Fernanda Borges, 2011, J. Chem. Inf. Model..

Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

Maykel Cruz-Monteagudo, Fernanda Borges, Humberto González Díaz, 2007, J. Comput. Chem..

Ensemble-based modeling of chemical compounds with antimalarial activity.

Maykel Cruz-Monteagudo, Fernanda Borges, Emilio Benfenati, 2019, Current topics in medicinal chemistry.

Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model.

Maykel Cruz-Monteagudo, Humberto González-Díaz, Eugenio Uriarte, 2005, Bioorganic & medicinal chemistry.

MOOP-DESIRE-based Simultaneous Optimization of the Analgesic, Antiinflammatory, and Ulcerogenic Profiles of 3-(3-Methylphenyl)-2- Substituted Amino-3H-Quinazolin-4-ones

M. Cruz-Monteagudo, Fernanda Borges, M. Cordeiro, 2008 .

Desirability‐based multiobjective optimization for global QSAR studies: Application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles

M. Natália D. S. Cordeiro, Maykel Cruz-Monteagudo, Fernanda Borges, 2008, J. Comput. Chem..

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Humbert González-Díaz, Maykel Cruz-Monteagudo, Alejandro Pazos, 2018, Scientific Reports.

A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents

Maykel Cruz-Monteagudo, Eduardo Tejera, Yunierkis Perez-Castillo, 2018, PloS one.

SMANN: AutoML Screening Model of Artificial Neural Networks for Brain Connectome

Humbert González-Díaz, Maykel Cruz-Monteagudo, Cristian R. Munteanu, 2017 .

Research Highlights : We introduce a new concept , the systemic chemogenomics / QSAR

José L. Medina-Franco, Maykel Cruz-Monteagudo, Stephan Schürer, 2017 .

M. Cruz-Monteagudo, Fernanda Borges, Hai PhamThe, 2010, Molecular informatics.

Global antifungal profile optimization of chlorophenyl derivatives against Botrytis cinerea and Colletotrichum gloeosporioides.

Maykel Cruz-Monteagudo, M. Cruz-Monteagudo, Liane Saíz-urra, 2009, Journal of agricultural and food chemistry.

Unify Markov model for Rational Design and Synthesis of More Safe Drugs. Predicting Multiple Drugs Side Effects

Maykel Cruz-Monteagudo, Yunierkis Pérez-Castillo, Reinaldo Molina-Ruiz, 2005 .

Efficient and biologically relevant consensus strategy for Parkinson’s disease gene prioritization

M. Natália D. S. Cordeiro, Maykel Cruz-Monteagudo, Eduardo Tejera, 2016, BMC Medical Genomics.

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

M. Cruz-Monteagudo, Elena Rosa Domínguez, Carlos Morell, 2008, Journal of combinatorial chemistry.

Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?

Maykel Cruz-Monteagudo, Ann Nowé, Yunierkis Pérez-Castillo, 2014, Molecular Diversity.