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

a Applied Chemistry Research Center, Central University of “Las Villas”, Santa Clara, 54830, Cuba b Chemical Bioactives Center, Central University of “Las Villas”, Santa Clara, 54830, Cuba c Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, 15782, Spain d Deparment of pharmacy and pharmaceutical technology, University of Velencia, Valencia, Spain. e Universitat Rostock, FB Chemie, Albert-Einstein-Str. 3a, D 18059 Rostock, Germany.

[1]  Emilio Benfenati,et al.  QSAR modelling of aldehyde toxicity by means of optimisation of correlation weights of nearest neighbouring codes , 2004 .

[2]  D. Lewis.  Computer‐Assisted methods in the evaluation of chemical toxicity , 2007 .

[3]  Maykel Pérez González,et al.  A topological sub-structural approach of the mutagenic activity in dental monomers. 1. Aromatic epoxides , 2004 .

[4]  Andrey A. Toropov,et al.  Modeling of acyclic carbonyl compounds normal boiling points by correlation weighting of nearest neighboring codes , 2002 .

[5]  Ernesto Estrada,et al.  Generalization of topological indices , 2001 .

[6]  Maykel Pérez González,et al.  TOPS-MODE approach to predict mutagenicity in dental monomers , 2004 .

[7]  D. Roberts.  An Analysis of Published Data on Fish Toxicity of Nitrobenzene and Aniline Derivatives , 1987 .

[8]  Zhirong Sun,et al.  Support vector machine approach for protein subcellular localization prediction , 2001, Bioinform..

[9]  J A McCammon,et al.  Computational science new horizons and relevance to pharmaceutical design. , 1996, Trends in cardiovascular medicine.

[10]  Eugenio Uriarte,et al.  Markovian Backbone Negentropies: Molecular descriptors for protein research. I. Predicting protein stability in Arc repressor mutants , 2004, Proteins.

[11]  K. Chou.  Prediction and classification of α‐turn types , 1997 .

[12]  Y. Martin,et al.  Quantitative drug design , 1978 .

[13]  H. Jeffreys.  A Treatise on Probability , 1922, Nature.

[14]  N. Stiefl,et al.  Mapping property distributions of molecular surfaces: algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique. , 2003, Journal of medicinal chemistry.

[15]  Jean Garnier,et al.  FORESST: fold recognition from secondary structure predictions of proteins , 1999, Bioinform..

[16]  Humberto González Díaz,et al.  Stochastic molecular descriptors for polymers. 1. Modelling the properties of icosahedral viruses with 3D-Markovian negentropies , 2004 .

[17]  M. Borodovsky,et al.  Detection of new genes in a bacterial genome using Markov models for three gene classes. , 1995, Nucleic acids research.

[18]  Miguel A. Cabrera,et al.  Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds , 2003, Journal of molecular modeling.

[19]  M. A. Cabrera Pérez,et al.  A novel approach to determining physicochemical and absorption properties of 6-fluoroquinolone derivatives: experimental assessment. , 2002, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[20]  M T Cronin,et al.  Quantitative structure-activity relationships as a tool to assess the comparative toxicity of organic chemicals. , 1995, Chemosphere.

[21]  T R Miller,et al.  Principles of therapeutics. , 1992, The Veterinary clinics of North America. Equine practice.

[22]  T. Hubbard,et al.  Fold recognition and ab initio structure predictions using hidden markov models and β‐strand pair potentials , 1995, Proteins.

[23]  Challenges for medicinal chemistry , 1996 .

[24]  M. A. Cabrera Pérez,et al.  In silico prediction of central nervous system activity of compounds. Identification of potential pharmacophores by the TOPS-MODE approach. , 2004, Bioorganic & medicinal chemistry.

[25]  Bruce R. Kowalski,et al.  31 Pattern recognition in chemistry , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[26]  Han van de Waterbeemd,et al.  Chemometric methods in molecular design , 1995 .

[27]  Reacciones adversas a los fármacos , 2003 .

[28]  Humberto González Díaz,et al.  Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides , 2002, Journal of molecular modeling.

[29]  Aalt Bast,et al.  Comprehensive medicinal chemistry , 1991 .

[30]  Yovani Marrero-Ponce,et al.  Linear Indices of the "Molecular Pseudograph's Atom Adjacency Matrix": Definition, Significance-Interpretation, and Application to QSAR Analysis of Flavone Derivatives as HIV-1 Integrase Inhibitors , 2004, J. Chem. Inf. Model..

[31]  T. Pöschel,et al.  Stochastic Processes in Physics, Chemistry, and Biology , 2000 .

[32]  Andrey A. Toropov,et al.  Prediction of Aquatic Toxicity: Use of Optimization of Correlation Weights of Local Graph Invariants , 2003, J. Chem. Inf. Comput. Sci..

[33]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[34]  Rafael Molina,et al.  Stochastic molecular descriptors for polymers. 2. Spherical truncation of electrostatic interactions , 2005 .

[35]  H. Díaz,et al.  A TOPS-MODE approach to predict permeability coefficients , 2004 .

[36]  K. Chou.  Prediction of signal peptides using scaled window , 2001, Peptides.

[37]  Prescripción racional de fármacos , 1983 .

[38]  A. Toropova,et al.  Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants , 2001 .

[39]  Humberto González-Díaz,et al.  Predicting stability of Arc repressor mutants with protein stochastic moments. , 2005, Bioorganic & medicinal chemistry.

[40]  Alessandra Villa,et al.  Sampling and convergence in free energy calculations of protein-ligand interactions: The binding of triphenoxypyridine derivatives to factor Xa and trypsin , 2003, J. Comput. Aided Mol. Des..

[41]  Maykel Pérez González,et al.  A novel approach to predict a toxicological property of aromatic compounds in the Tetrahymena pyriformis. , 2004, Bioorganic & medicinal chemistry.

[42]  Humberto González Díaz,et al.  Simple stochastic fingerprints towards mathematical modelling in biology and medicine. 1. The treatment of coccidiosis , 2004, Bulletin of mathematical biology.

[43]  Yovani Marrero-Ponce,et al.  Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds. , 2005, Bioorganic & medicinal chemistry.

[44]  Humberto González Díaz,et al.  Symmetry considerations in Markovian chemicals 'in silico' design (MARCH-INSIDE) I: central chirality codification, classification of ACE inhibitors and prediction of \sigma-receptor antagonist activities , 2003, Comput. Biol. Chem..

[45]  Humberto González-Díaz,et al.  Markov entropy backbone electrostatic descriptors for predicting proteins biological activity. , 2004, Bioorganic & medicinal chemistry letters.

[46]  Kuo-Chen Chou,et al.  Prediction of protein signal sequences. , 2002, Current protein & peptide science.

[47]  Andrey A. Toropov,et al.  QSPR modeling of alkanes properties based on graph of atomic orbitals , 2003 .

[48]  Francisco Torrens,et al.  3D-chiral quadratic indices of the 'molecular pseudograph's atom adjacency matrix' and their application to central chirality codification: classification of ACE inhibitors and prediction of sigma-receptor antagonist activities. , 2004, Bioorganic & medicinal chemistry.

[49]  Eugenio Uriarte,et al.  Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides. , 2004, Bioorganic & medicinal chemistry.

[50]  Humberto González Díaz,et al.  Markovian negentropies in bioinformatics. 1. A picture of footprints after the interaction of the HIV-1 -RNA packaging region with drugs , 2003, Bioinform..

[51]  D. Haussler,et al.  Hidden Markov models in computational biology. Applications to protein modeling. , 1993, Journal of molecular biology.

[52]  K. Chou,et al.  A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. , 1993, The Journal of biological chemistry.

[53]  Ronal Ramos de Armas,et al.  Vibrational Markovian modelling of footprints after the interaction of antibiotics with the packaging region of HIV type 1 , 2003, Bulletin of mathematical biology.

[54]  E V Koonin,et al.  New genes in old sequence: a strategy for finding genes in the bacterial genome. , 1994, Trends in biochemical sciences.

[55]  Mark T. D. Cronin.  Computer‐aided Prediction of Drug Toxicity in High Throughput Screening , 1998 .