Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: challenges and breakthroughs.

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

[1]  Stefano Moro,et al.  Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR-Ligand Recognition Pathway in a Nanosecond Time Scale , 2014, J. Chem. Inf. Model..

[2]  Chao Wu,et al.  Computational drug repositioning through heterogeneous network clustering , 2013, BMC Systems Biology.

[3]  Maria Kontoyianni,et al.  Evaluation of docking performance: comparative data on docking algorithms. , 2004, Journal of medicinal chemistry.

[4]  Holger Gohlke,et al.  Binding Region of Alanopine Dehydrogenase Predicted by Unbiased Molecular Dynamics Simulations of Ligand Diffusion , 2013, J. Chem. Inf. Model..

[5]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[6]  P. Sanseau,et al.  Drug repurposing: progress, challenges and recommendations , 2018, Nature Reviews Drug Discovery.

[7]  Yanan Jiang,et al.  Neddylation Pathway as a Novel Anti-cancer Target: Mechanistic Investigation and Therapeutic Implication. , 2015, Anti-Cancer Agents in Medicinal Chemistry.

[8]  A. Mark,et al.  Fluctuation and cross-correlation analysis of protein motions observed in nanosecond molecular dynamics simulations. , 1995, Journal of molecular biology.

[9]  Dariusz Plewczynski,et al.  Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database , 2011, J. Comput. Chem..

[10]  J. Skolnick,et al.  Comprehensive prediction of drug-protein interactions and side effects for the human proteome , 2015, Scientific Reports.

[11]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[12]  R. Thaimattam,et al.  Protein kinase inhibitors: structural insights into selectivity. , 2007, Current pharmaceutical design.

[13]  S. Cusack,et al.  Structures of the inactive and active states of RIP2 kinase inform on the mechanism of activation , 2017, PloS one.

[14]  G. Brewer Drug development for orphan diseases in the context of personalized medicine. , 2009, Translational research : the journal of laboratory and clinical medicine.

[15]  Kiattawee Choowongkomon,et al.  Computational study of EGFR inhibition: molecular dynamics studies on the active and inactive protein conformations , 2013, Journal of Molecular Modeling.

[16]  C. Telleria Drug Repurposing for Cancer Therapy. , 2012, Journal of cancer science & therapy.

[17]  Michael Karpusas,et al.  Analysis of imatinib and sorafenib binding to p38alpha compared with c-Abl and b-Raf provides structural insights for understanding the selectivity of inhibitors targeting the DFG-out form of protein kinases. , 2010, Biochemistry.

[18]  Ramaiah Muthyala,et al.  Orphan/rare drug discovery through drug repositioning , 2011 .

[19]  H. Hsieh,et al.  Drug repurposing for chronic myeloid leukemia: in silico and in vitro investigation of DrugBank database for allosteric Bcr-Abl inhibitors , 2017, Journal of biomolecular structure & dynamics.

[20]  Dan Li,et al.  Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. , 2016, Physical chemistry chemical physics : PCCP.

[21]  Farzin Sohraby,et al.  Complete reconstruction of the unbinding pathway of an anticancer drug by conventional unbiased molecular dynamics simulation , 2020, bioRxiv.

[22]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[23]  Hassan Aryapour,et al.  In silico prediction of new inhibitors for the nucleotide pool sanitizing enzyme, MTH1, using drug repurposing , 2018, Journal of biomolecular structure & dynamics.

[24]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[25]  M Rarey,et al.  Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.

[26]  M Karplus,et al.  Solvent effects on protein motion and protein effects on solvent motion. Dynamics of the active site region of lysozyme. , 1989, Journal of molecular biology.

[27]  W L Jorgensen,et al.  Rusting of the lock and key model for protein-ligand binding. , 1991, Science.

[28]  Philippe Sanseau,et al.  Editorial: Computational methods for drug repurposing , 2011, Briefings Bioinform..

[29]  R. Roskoski Classification of small molecule protein kinase inhibitors based upon the structures of their drug-enzyme complexes. , 2016, Pharmacological research.

[30]  George D Geromichalos Importance of molecular computer modeling in anticancer drug development. , 2007, Journal of B.U.ON. : official journal of the Balkan Union of Oncology.

[31]  J. Gready,et al.  Combining docking and molecular dynamic simulations in drug design , 2006, Medicinal research reviews.

[32]  Todd J. A. Ewing,et al.  Critical evaluation of search algorithms for automated molecular docking and database screening , 1997 .

[33]  Y. Martin,et al.  A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.

[34]  E. Lionta,et al.  Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances , 2014, Current topics in medicinal chemistry.

[35]  Sahdeo Prasad,et al.  Cancer drug discovery by repurposing: teaching new tricks to old dogs. , 2013, Trends in pharmacological sciences.

[36]  R. Dror,et al.  Long-timescale molecular dynamics simulations of protein structure and function. , 2009, Current opinion in structural biology.

[37]  Kranthi Kumar Konidala,et al.  Structural Probing, Screening and Structure-Based Drug Repositioning Insights into the Identification of Potential Cox-2 Inhibitors from Selective Coxibs , 2017, Interdisciplinary Sciences: Computational Life Sciences.

[38]  Yanli Wang,et al.  Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review , 2012, The AAPS Journal.

[39]  L. Pardo,et al.  The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor , 2016, Scientific Reports.

[40]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[41]  Robert J. Doerksen,et al.  Docking Challenge: Protein Sampling and Molecular Docking Performance , 2013, J. Chem. Inf. Model..

[42]  D. Webb,et al.  Are rare diseases still orphans or happily adopted? The challenges of developing and using orphan medicinal products. , 2006, British journal of clinical pharmacology.

[43]  Sandrine Gerber-Lemaire,et al.  Evaluation of docking programs for predicting binding of Golgi α‐mannosidase II inhibitors: A comparison with crystallography , 2007, Proteins.

[44]  I. Kuntz,et al.  Ligand solvation in molecular docking , 1999, Proteins.

[45]  Vassilis Virvilis,et al.  Literature mining, ontologies and information visualization for drug repurposing , 2011, Briefings Bioinform..

[46]  Heather A Carlson,et al.  Incorporating protein flexibility in structure-based drug discovery: using HIV-1 protease as a test case. , 2004, Journal of the American Chemical Society.

[47]  J P Changeux,et al.  On the nature of allosteric transitions: implications of non-exclusive ligand binding. , 1966, Journal of molecular biology.

[48]  A. Barabasi,et al.  Network-based in silico drug efficacy screening , 2016, Nature Communications.

[49]  Paul D Lyne,et al.  Structure-based virtual screening: an overview. , 2002, Drug discovery today.

[50]  Tudor I. Oprea,et al.  Drug Repurposing: Far Beyond New Targets for Old Drugs , 2012, The AAPS Journal.

[51]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[52]  B. Padhy,et al.  Drug repositioning: re-investigating existing drugs for new therapeutic indications. , 2011, Journal of postgraduate medicine.

[53]  Pickett,et al.  Computational methods for the prediction of 'drug-likeness' , 2000, Drug discovery today.

[54]  Ola Engkvist,et al.  On the Integration of In Silico Drug Design Methods for Drug Repurposing , 2017, Front. Pharmacol..

[55]  Ajay N. Jain,et al.  Scoring functions for protein-ligand docking. , 2006, Current protein & peptide science.

[56]  Umesh Yadava,et al.  Stabilization of Microtubules by Taxane Diterpenoids: Insight from Docking and MD simulations , 2015, Journal of biological physics.

[57]  Weida Tong,et al.  In silico drug repositioning: what we need to know. , 2013, Drug discovery today.

[58]  Jeffrey Aubé,et al.  Drug repurposing and the medicinal chemist. , 2012, ACS medicinal chemistry letters.

[59]  D. Rognan,et al.  Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.

[60]  James Andrew McCammon,et al.  Conformational Sampling and Nucleotide-Dependent Transitions of the GroEL Subunit Probed by Unbiased Molecular Dynamics Simulations , 2011, PLoS Comput. Biol..

[61]  V. Pande,et al.  Activation pathway of Src kinase reveals intermediate states as novel targets for drug design , 2014, Nature Communications.

[62]  Weiliang Zhu,et al.  Molecular docking for drug discovery and development: a widely used approach but far from perfect. , 2016, Future medicinal chemistry.

[63]  Xiaoqin Zou,et al.  Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design , 2014, Molecules.

[64]  B. Berne,et al.  How and when does an anticancer drug leave its binding site? , 2017, Science Advances.

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

[66]  Renxiao Wang,et al.  Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.

[67]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[68]  Eric T. Kim,et al.  How does a drug molecule find its target binding site? , 2011, Journal of the American Chemical Society.

[69]  Xiaoqin Zou,et al.  Advances and Challenges in Protein-Ligand Docking , 2010, International journal of molecular sciences.

[70]  Reiji Teramoto,et al.  Supervised Consensus Scoring for Docking and Virtual Screening , 2007, J. Chem. Inf. Model..

[71]  I. Ghosh,et al.  New directions in targeting protein kinases: focusing upon true allosteric and bivalent inhibitors. , 2012, Current pharmaceutical design.

[72]  K. Gajiwala,et al.  Insights into the aberrant activity of mutant EGFR kinase domain and drug recognition. , 2013, Structure.

[73]  W. L. Jorgensen The Many Roles of Computation in Drug Discovery , 2004, Science.

[74]  W. Tong,et al.  Computational drug repositioning for rare diseases in the era of precision medicine. , 2017, Drug discovery today.

[75]  Kevan M. Shokat,et al.  Overcoming Resistance to HER2 Inhibitors Through State-Specific Kinase Binding , 2016, Nature chemical biology.

[76]  H. Frauenfelder,et al.  Slaving: Solvent fluctuations dominate protein dynamics and functions , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[77]  Samuel D. Lotz,et al.  Unbiased Molecular Dynamics of 11 min Timescale Drug Unbinding Reveals Transition State Stabilizing Interactions. , 2018, Journal of the American Chemical Society.

[78]  Claudio N. Cavasotto,et al.  Ligand docking and structure-based virtual screening in drug discovery. , 2007, Current topics in medicinal chemistry.

[79]  M. Boguski,et al.  Repurposing with a Difference , 2009, Science.

[80]  Jie Li,et al.  Review of Drug Repositioning Approaches and Resources , 2018, International journal of biological sciences.

[81]  R Sánchez,et al.  Advances in comparative protein-structure modelling. , 1997, Current opinion in structural biology.

[82]  J. Kuriyan,et al.  The Conformational Plasticity of Protein Kinases , 2002, Cell.

[83]  M. Mezei,et al.  Molecular docking: a powerful approach for structure-based drug discovery. , 2011, Current computer-aided drug design.

[84]  Yongbo Hu,et al.  Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy , 2009, J. Chem. Inf. Model..

[85]  James Andrew McCammon,et al.  Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening , 2011, J. Chem. Inf. Model..

[86]  B. Matthews,et al.  A model binding site for testing scoring functions in molecular docking. , 2002, Journal of molecular biology.

[87]  Jacob D. Durrant,et al.  Molecular dynamics simulations and drug discovery , 2011, BMC Biology.

[88]  R. Clark,et al.  Consensus scoring for ligand/protein interactions. , 2002, Journal of molecular graphics & modelling.

[89]  J. Berg,et al.  Molecular dynamics simulations of biomolecules , 2002, Nature Structural Biology.

[90]  Edward W. Lowe,et al.  Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.

[91]  Arvin C Dar,et al.  Small molecule recognition of c-Src via the Imatinib-binding conformation. , 2008, Chemistry & biology.

[92]  R. Nussinov,et al.  The role of dynamic conformational ensembles in biomolecular recognition. , 2009, Nature chemical biology.

[93]  Farzin Sohraby,et al.  Study of the binding mechanism of dasatinib to c-Src kinase using an efficient molecular dynamics method , 2020 .

[94]  M. Moses,et al.  Two decades of orphan product development , 2002, Nature Reviews Drug Discovery.

[95]  G. Drummen,et al.  International Journal of Molecular Sciences G Protein-coupled Receptors in Cancer , 2022 .

[96]  V. Gordeliy,et al.  Two Distinct States of the HAMP Domain from Sensory Rhodopsin Transducer Observed in Unbiased Molecular Dynamics Simulations , 2013, PloS one.

[97]  H. Wolfson,et al.  FiberDock: Flexible induced‐fit backbone refinement in molecular docking , 2010, Proteins.

[98]  L. Tong,et al.  Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site , 2002, Nature Structural Biology.

[99]  M. Najafi,et al.  Cyclooxygenase‐2 in cancer: A review , 2018, Journal of cellular physiology.

[100]  L. Johnson,et al.  Protein Kinase Inhibitors: Insights into Drug Design from Structure , 2004, Science.

[101]  Victor Guallar,et al.  Importance of accurate charges in molecular docking: Quantum mechanical/molecular mechanical (QM/MM) approach , 2005, J. Comput. Chem..

[102]  A. Mantel‐Teeuwisse,et al.  Drug repositioning and repurposing: terminology and definitions in literature. , 2015, Drug discovery today.

[103]  Torsten Schwede,et al.  BIOINFORMATICS Bioinformatics Advance Access published November 12, 2005 The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling , 2022 .

[104]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[105]  Xiaoqin Zou,et al.  Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein-Ligand Interactions , 2010, J. Chem. Inf. Model..

[106]  Polina Mamoshina,et al.  Design of efficient computational workflows for in silico drug repurposing. , 2017, Drug discovery today.

[107]  Stephen T. C. Wong,et al.  Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. , 2014, Drug discovery today.

[108]  Shuichi Hirono,et al.  Comparison of Consensus Scoring Strategies for Evaluating Computational Models of Protein-Ligand Complexes , 2006, J. Chem. Inf. Model..

[109]  Miklos Feher,et al.  Consensus scoring for protein-ligand interactions. , 2006, Drug discovery today.

[110]  Khader Shameer,et al.  In silico methods for drug repurposing and pharmacology , 2016, Wiley interdisciplinary reviews. Systems biology and medicine.

[111]  Sean Ekins,et al.  A bibliometric review of drug repurposing. , 2018, Drug discovery today.

[112]  Khader Shameer,et al.  Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. , 2015, Current topics in medicinal chemistry.

[113]  Didier Rognan,et al.  Comparative evaluation of eight docking tools for docking and virtual screening accuracy , 2004, Proteins.

[114]  G. Klebe,et al.  Knowledge-based scoring function to predict protein-ligand interactions. , 2000, Journal of molecular biology.

[115]  Hassan Aryapour,et al.  Repurposing existing drugs for new AMPK activators as a strategy to extend lifespan: a computer-aided drug discovery study , 2018, Biogerontology.

[116]  Zhiyong Lu,et al.  A survey of current trends in computational drug repositioning , 2016, Briefings Bioinform..

[117]  Cheng Zhu,et al.  Drug repositioning for orphan diseases , 2011, Briefings Bioinform..

[118]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[119]  Ajay N. Jain Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities , 1996, J. Comput. Aided Mol. Des..

[120]  D. J. Price,et al.  Assessing scoring functions for protein-ligand interactions. , 2004, Journal of medicinal chemistry.

[121]  K. Misura,et al.  PROTEINS: Structure, Function, and Bioinformatics 59:15–29 (2005) Progress and Challenges in High-Resolution Refinement of Protein Structure Models , 2022 .

[122]  Martin Stahl,et al.  Binding site characteristics in structure-based virtual screening: evaluation of current docking tools , 2003, Journal of molecular modeling.

[123]  Benoît Roux,et al.  Computational Study of Gleevec and G6G Reveals Molecular Determinants of Kinase Inhibitor Selectivity , 2014, Journal of the American Chemical Society.

[124]  M. Karplus,et al.  Dynamics of folded proteins , 1977, Nature.

[125]  Maruti J. Dhanavade,et al.  Homology modeling, molecular docking and MD simulation studies to investigate role of cysteine protease from Xanthomonas campestris in degradation of Aβ peptide , 2013, Comput. Biol. Medicine.

[126]  Klaus Schulten,et al.  Domain motion of individual F1-ATPase β-subunits during unbiased molecular dynamics simulations. , 2011, The journal of physical chemistry. A.

[127]  D. Frank Hsu,et al.  Consensus Scoring Criteria for Improving Enrichment in Virtual Screening , 2005, J. Chem. Inf. Model..

[128]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[129]  M. Karplus,et al.  Molecular dynamics simulations in biology , 1990, Nature.

[130]  Ajay N. Jain Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. , 2003, Journal of medicinal chemistry.

[131]  Ravi Radhakrishnan,et al.  Erlotinib binds both inactive and active conformations of the EGFR tyrosine kinase domain , 2012, The Biochemical journal.

[132]  Shaomeng Wang,et al.  How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment , 2001, J. Chem. Inf. Comput. Sci..

[133]  R. Nussinov,et al.  Folding funnels, binding funnels, and protein function , 1999, Protein science : a publication of the Protein Society.

[134]  M. Clausen,et al.  Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. , 2016, Drug discovery today.

[135]  Ray Luo,et al.  Virtual screening using molecular simulations , 2011, Proteins.

[136]  Suryanarayana Yaddanapudi,et al.  Changing Trends in Computational Drug Repositioning , 2018, Pharmaceuticals.

[137]  Christos Boutsidis,et al.  Atomic-level characterization of the ensemble of the Aβ(1-42) monomer in water using unbiased molecular dynamics simulations and spectral algorithms. , 2011, Journal of molecular biology.

[138]  A. Anderson The process of structure-based drug design. , 2003, Chemistry & biology.

[139]  Matthew P Jacobson,et al.  Turning a protein kinase on or off from a single allosteric site via disulfide trapping , 2011, Proceedings of the National Academy of Sciences.

[140]  S. Sleigh,et al.  Repurposing Strategies for Therapeutics , 2010, Pharmaceutical Medicine.

[141]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[142]  Farzin Sohraby,et al.  Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation. , 2018, Methods in molecular biology.

[143]  Yong-Jun Jiang,et al.  Molecular docking and molecular dynamics simulation studies of GPR40 receptor-agonist interactions. , 2010, Journal of molecular graphics & modelling.

[144]  Kaare Teilum,et al.  Protein stability, flexibility and function. , 2011, Biochimica et biophysica acta.

[145]  Daniel A. Gschwend,et al.  Molecular docking towards drug discovery , 1996, Journal of molecular recognition : JMR.

[146]  Zhihai Liu,et al.  Evaluation of the performance of four molecular docking programs on a diverse set of protein‐ligand complexes , 2010, J. Comput. Chem..

[147]  Weiliang Zhu,et al.  Repositioning organohalogen drugs: a case study for identification of potent B-Raf V600E inhibitors via docking and bioassay , 2016, Scientific Reports.

[148]  Campbell McInnes,et al.  Virtual screening strategies in drug discovery. , 2007, Current opinion in chemical biology.

[149]  Nicolas Moitessier,et al.  Docking Ligands into Flexible and Solvated Macromolecules. 4. Are Popular Scoring Functions Accurate for this Class of Proteins? , 2009, J. Chem. Inf. Model..

[150]  Pekka Tiikkainen,et al.  Computational tools for polypharmacology and repurposing. , 2011, Future medicinal chemistry.

[151]  I. Kuntz Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[152]  Philip E. Bourne,et al.  A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing , 2011, J. Chem. Inf. Model..

[153]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[154]  G. de Fabritiis,et al.  Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations , 2011, Proceedings of the National Academy of Sciences.

[155]  James R. Burke,et al.  BMS-345541 Is a Highly Selective Inhibitor of IκB Kinase That Binds at an Allosteric Site of the Enzyme and Blocks NF-κB-dependent Transcription in Mice* , 2003, The Journal of Biological Chemistry.

[156]  J. Irwin,et al.  Lead discovery using molecular docking. , 2002, Current opinion in chemical biology.

[157]  Walter Filgueira de Azevedo,et al.  Molecular docking algorithms. , 2008, Current drug targets.

[158]  C L Verlinde,et al.  Structure-based drug design: progress, results and challenges. , 1994, Structure.

[159]  J. Mestan,et al.  Allosteric inhibitors of Bcr-abl–dependent cell proliferation , 2006, Nature chemical biology.

[160]  R. Dror,et al.  A conserved protonation-dependent switch controls drug binding in the Abl kinase , 2009, Proceedings of the National Academy of Sciences.

[161]  V. Sukhatme,et al.  Drug repurposing in oncology—patient and health systems opportunities , 2015, Nature Reviews Clinical Oncology.

[162]  Farzin Sohraby,et al.  A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: Probing the binding pathway of dasatinib to Src-kinase , 2020, Bioinform..

[163]  J. An,et al.  Structure-based virtual screening of chemical libraries for drug discovery. , 2006, Current opinion in chemical biology.

[164]  Tudor I Oprea,et al.  Computational and Practical Aspects of Drug Repositioning. , 2015, Assay and drug development technologies.

[165]  D. Chan,et al.  Structure-based repurposing of FDA-approved drugs as inhibitors of NEDD8-activating enzyme. , 2014, Biochimie.

[166]  Yi-Ping Phoebe Chen,et al.  Structure-based drug design to augment hit discovery. , 2011, Drug discovery today.

[167]  Albert C. Pan,et al.  Pathway and mechanism of drug binding to G-protein-coupled receptors , 2011, Proceedings of the National Academy of Sciences.

[168]  Paul A. Insel,et al.  G Protein-Coupled Receptors as Targets for Approved Drugs: How Many Targets and How Many Drugs? , 2018, Molecular Pharmacology.

[169]  HemaSree Gns,et al.  An update on Drug Repurposing: Re-written saga of the drug's fate. , 2019, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[170]  Haizhen Zhong,et al.  Induced-fit docking studies of the active and inactive states of protein tyrosine kinases. , 2009, Journal of molecular graphics & modelling.

[171]  Hassan Aryapour,et al.  In silico drug repurposing of FDA-approved drugs to predict new inhibitors for drug resistant T315I mutant and wild-type BCR-ABL1: A virtual screening and molecular dynamics study. , 2017, Journal of molecular graphics & modelling.

[172]  S. Ekins,et al.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling , 2007, British journal of pharmacology.

[173]  Vigneshwaran Namasivayam,et al.  Research Article: pso@autodock: A Fast Flexible Molecular Docking Program Based on Swarm Intelligence , 2007, Chemical biology & drug design.

[174]  Arrigo Schieppati,et al.  Why rare diseases are an important medical and social issue , 2008, The Lancet.

[175]  Susan S. Taylor,et al.  Surface comparison of active and inactive protein kinases identifies a conserved activation mechanism , 2006, Proceedings of the National Academy of Sciences.