Computational Predictions for Multi-Target Drug Design

[1]  Philip E. Bourne,et al.  SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison , 2010, Nucleic Acids Res..

[2]  K. Chou,et al.  Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. , 2008, Bioorganic & medicinal chemistry.

[3]  Apilak Worachartcheewan,et al.  AutoWeka: toward an automated data mining software for QSAR and QSPR studies. , 2015, Methods in molecular biology.

[4]  B. Jayaram,et al.  DNA Binding Studies of Vinca Alkaloids: Experimental and Computational Evidence , 2012, Natural product communications.

[5]  M. Verdonk,et al.  A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. , 2007, Current topics in medicinal chemistry.

[6]  Max W. Chang,et al.  Virtual Screening for HIV Protease Inhibitors: A Comparison of AutoDock 4 and Vina , 2010, PloS one.

[7]  Andrew E. Torda,et al.  The GROMOS biomolecular simulation program package , 1999 .

[8]  J. Bajorath,et al.  Polypharmacology: challenges and opportunities in drug discovery. , 2014, Journal of medicinal chemistry.

[9]  A. Masum,et al.  Binding of DNA with Rhodamine B: Spectroscopic and molecular modeling studies , 2013 .

[10]  M. Oechsner,et al.  COMT-inhibition increases serum levels of dihydroxyphenylacetic acid (DOPAC) in patients with advanced Parkinson's disease , 2002, Journal of Neural Transmission.

[11]  D. van der Spoel,et al.  GROMACS: A message-passing parallel molecular dynamics implementation , 1995 .

[12]  B. Stockwell,et al.  Multicomponent therapeutics for networked systems , 2005, Nature Reviews Drug Discovery.

[13]  Martin Krug,et al.  Recent advances in the development of multi-kinase inhibitors. , 2008, Mini reviews in medicinal chemistry.

[14]  A. Caflisch,et al.  Molecular dynamics in drug design. , 2015, European journal of medicinal chemistry.

[15]  Ram Samudrala,et al.  Identification of potential multitarget antimalarial drugs. , 2005, JAMA.

[16]  Vladimir Poroikov,et al.  QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction , 2011, Molecular informatics.

[17]  Elizabeth Yuriev,et al.  Challenges and advances in computational docking: 2009 in review , 2011, Journal of molecular recognition : JMR.

[18]  Sean Ekins,et al.  In silico repositioning of approved drugs for rare and neglected diseases. , 2011, Drug discovery today.

[19]  Fu Wei,et al.  Evaluation of various inverse docking schemes in multiple targets identification. , 2010, Journal of molecular graphics & modelling.

[20]  T. Darden,et al.  Molecular dynamics simulations of biomolecules: long-range electrostatic effects. , 1999, Annual review of biophysics and biomolecular structure.

[21]  Sara Ballouz,et al.  Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction , 2016, PloS one.

[22]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[23]  K. Shokat,et al.  Escape from HER family tyrosine kinase inhibitor therapy by the kinase inactive HER3 , 2007, Nature.

[24]  Jeremy C. Smith,et al.  VinaMPI: Facilitating multiple receptor high‐throughput virtual docking on high‐performance computers , 2013, J. Comput. Chem..

[25]  Ram Samudrala,et al.  Computational Multitarget Drug Discovery , 2012 .

[26]  B. Garvik,et al.  Principles for the buffering of genetic variation. , 2001 .

[27]  Zhi Wang,et al.  Tinker‐OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs , 2017, J. Comput. Chem..

[28]  L. Verlet.  Computer "Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules , 1967 .

[29]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[30]  U. C. Halder,et al.  Thermodynamic study of rhodamine 123-calf thymus DNA interaction: determination of calorimetric enthalpy by optical melting study. , 2014, The journal of physical chemistry. B.

[31]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[32]  Paola Brun,et al.  Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. , 2011, European journal of medicinal chemistry.

[33]  Rommie E. Amaro,et al.  An improved relaxed complex scheme for receptor flexibility in computer-aided drug design , 2008, J. Comput. Aided Mol. Des..

[34]  G. Hortobagyi,et al.  Mechanisms of Disease: understanding resistance to HER2-targeted therapy in human breast cancer , 2006, Nature Clinical Practice Oncology.

[35]  Reinaldo Molina Ruiz,et al.  Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries. , 2008, Journal of combinatorial chemistry.

[36]  J. Andrew McCammon,et al.  Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei , 2008, Proceedings of the National Academy of Sciences.

[37]  Sanjay Joshua Swamidass,et al.  Mining small-molecule screens to repurpose drugs , 2011, Briefings Bioinform..

[38]  Marco De Vivo,et al.  The increasing role of QM/MM in drug discovery. , 2012, Advances in protein chemistry and structural biology.

[39]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[40]  Kunal Roy,et al.  Selected Statistical Methods in QSAR , 2015 .

[41]  Josep Tabernero,et al.  The Role of VEGF and EGFR Inhibition: Implications for Combining Anti–VEGF and Anti–EGFR Agents , 2007, Molecular Cancer Research.

[42]  G. S. Kumar,et al.  RNA targeting through binding of small molecules: Studies on t-RNA binding by the cytotoxic protoberberine alkaloid coralyne. , 2009, Molecular bioSystems.

[43]  Josep Ramón Goñi,et al.  Molecular dynamics simulations: advances and applications , 2015, Advances and applications in bioinformatics and chemistry : AABC.

[44]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[45]  Ram Samudrala,et al.  Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform. , 2016, Current pharmaceutical design.

[46]  Saher Afshan Shaikh,et al.  A swift all-atom energy-based computational protocol to predict DNA-ligand binding affinity and DeltaTm. , 2007, Journal of medicinal chemistry.

[47]  Andreas Zell,et al.  Inferring multi-target QSAR models with taxonomy-based multi-task learning , 2013, Journal of Cheminformatics.

[48]  L. Sacks,et al.  Challenges, successes and hopes in the development of novel TB therapeutics. , 2009, Future medicinal chemistry.

[49]  Ram Samudrala,et al.  Novel paradigms for drug discovery: computational multitarget screening. , 2008, Trends in pharmacological sciences.

[50]  Sarita Sarkar,et al.  Targeting different RNA motifs by beta carboline alkaloid, harmalol: a comparative photophysical, calorimetric, and molecular docking approach , 2016, Journal of biomolecular structure & dynamics.

[51]  Sarita Sarkar,et al.  Sequence Specific Binding of Beta Carboline Alkaloid Harmalol with Deoxyribonucleotides: Binding Heterogeneity, Conformational, Thermodynamic and Cytotoxic Aspects , 2014, PloS one.

[52]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[53]  Kui Xu,et al.  Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases , 2011, Briefings Bioinform..

[54]  Miguel A. Cabrera,et al.  Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species: distribution of the antimicrobial G1 on rat tissues. , 2005, Bioorganic & medicinal chemistry letters.

[55]  S. Pal,et al.  Molecular recognition pattern of cytotoxic alkaloid vinblastine with multiple targets. , 2014, Journal of molecular graphics & modelling.

[56]  V. Poroikov,et al.  Computer-aided prediction for medicinal chemistry via the Internet , 2008, SAR and QSAR in environmental research.

[57]  Yang Yang,et al.  Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials , 2011, Comput. Chem. Eng..

[58]  K. Chou,et al.  Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. , 2009, Bioorganic & medicinal chemistry.

[59]  G. S. Kumar,et al.  Binding of DNA-binding alkaloids berberine and palmatine to tRNA and comparison to ethidium: Spectroscopic and molecular modeling studies , 2008 .

[60]  G. S. Kumar,et al.  DNA minor groove binding of small molecules: Experimental and computational evidence , 2010 .

[61]  Ram Samudrala,et al.  CANDO and the infinite drug discovery frontier. , 2014, Drug discovery today.

[62]  J. Mccammon,et al.  Computational drug design accommodating receptor flexibility: the relaxed complex scheme. , 2002, Journal of the American Chemical Society.

[63]  N. Bonander,et al.  Relieving the first bottleneck in the drug discovery pipeline: using array technologies to rationalize membrane protein production , 2009, Expert review of proteomics.

[64]  Humberto González-Díaz,et al.  Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. , 2010, Bioorganic & medicinal chemistry.

[65]  Eugenio Uriarte,et al.  Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. , 2009, Molecular pharmaceutics.

[66]  Matthew Paul Gleeson,et al.  QM/MM Calculations in Drug Discovery: A Useful Method for Studying Binding Phenomena? , 2009, J. Chem. Inf. Model..

[67]  Alejandro Speck-Planche,et al.  Advanced In Silico Approaches for Drug Discovery: Mining Information from Multiple Biological and Chemical Data Through mtk- QSBER and pt-QSPR Strategies. , 2017, Current medicinal chemistry.

[68]  Pankaj Sharma,et al.  ParDOCK: an all atom energy based Monte Carlo docking protocol for protein-ligand complexes. , 2007, Protein and peptide letters.

[69]  John P. Overington,et al.  Can we rationally design promiscuous drugs? , 2006, Current opinion in structural biology.

[70]  Alejandro Speck-Planche,et al.  Multitasking models for quantitative structure–biological effect relationships: current status and future perspectives to speed up drug discovery , 2015, Expert opinion on drug discovery.

[71]  Zhi-Liang Ji,et al.  High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics. , 2014, Toxicology and applied pharmacology.

[72]  Y Z Chen,et al.  Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. , 2013, Current medicinal chemistry.

[73]  Peter Atkins,et al.  Physical Chemistry for the Life Sciences , 2005 .

[74]  Nohad Gresh,et al.  Tinker-HP: a massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc04531j , 2017, Chemical science.

[75]  S. Gillespie,et al.  XDR-TB, what is it; how is it treated; and why is therapeutic failure so high? , 2011, Recent patents on anti-infective drug discovery.