Recent Trends in In-silico Drug Discovery

A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed.

[1]  Hsuan-Cheng Huang,et al.  Reviewing Ligand-Based Rational Drug Design: The Search for an ATP Synthase Inhibitor , 2011, International journal of molecular sciences.

[2]  R. Read,et al.  A multiple-start Monte Carlo docking method. , 1992, Proteins.

[3]  P. Ehrlich Über den jetzigen Stand der Chemotherapie , 1909 .

[4]  E. Shakhnovich,et al.  SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence , 1996 .

[5]  Garland R. Marshall,et al.  VALIDATE: A New Method for the Receptor-Based Prediction of Binding Affinities of Novel Ligands , 1996 .

[6]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998, J. Comput. Chem..

[7]  Hans-Joachim Böhm,et al.  Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs , 1998, J. Comput. Aided Mol. Des..

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

[9]  J. Topliss Some observations on classical QSAR , 1993 .

[10]  A. Hopfinger,et al.  Methods for applying the quantitative structure-activity relationship paradigm. , 2004, Methods in molecular biology.

[11]  Gennady Verkhivker,et al.  Deciphering common failures in molecular docking of ligand-protein complexes , 2000, J. Comput. Aided Mol. Des..

[12]  Mihaly Mezei,et al.  A new method for mapping macromolecular topography. , 2003, Journal of molecular graphics & modelling.

[13]  G. Ginsburg,et al.  Personalized medicine: revolutionizing drug discovery and patient care. , 2001, Trends in biotechnology.

[14]  R. Laskowski SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. , 1995, Journal of molecular graphics.

[15]  B. Shoichet,et al.  Soft docking and multiple receptor conformations in virtual screening. , 2004, Journal of medicinal chemistry.

[16]  Soma Mandal,et al.  Rational drug design. , 2009, European journal of pharmacology.

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

[18]  J. Briggs,et al.  Structure-based drug design: computational advances. , 1997, Annual review of pharmacology and toxicology.

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

[20]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[21]  M Karplus,et al.  HOOK: A program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site , 1994, Proteins.

[22]  Hans-Joachim Böhm,et al.  LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads , 1992, J. Comput. Aided Mol. Des..

[23]  Peter A. Kollman,et al.  FREE ENERGY CALCULATIONS : APPLICATIONS TO CHEMICAL AND BIOCHEMICAL PHENOMENA , 1993 .

[24]  Jonathan W. Essex,et al.  A review of protein-small molecule docking methods , 2002, J. Comput. Aided Mol. Des..

[25]  J. Scott Dixon,et al.  Flexible ligand docking using a genetic algorithm , 1995, J. Comput. Aided Mol. Des..

[26]  I. Kuntz,et al.  Structure-based discovery of inhibitors of thymidylate synthase. , 1993, Science.

[27]  Jens Meiler,et al.  ROSETTALIGAND: Protein–small molecule docking with full side‐chain flexibility , 2006, Proteins.

[28]  A. D. L. Nuez,et al.  Current methodology for the assessment of ADME-Tox properties on drug candidate molecules , 2008 .

[29]  Tomasz Arodz,et al.  Computational methods in developing quantitative structure-activity relationships (QSAR): a review. , 2006, Combinatorial chemistry & high throughput screening.

[30]  Thomas Lengauer,et al.  Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking , 1999, Proteins.

[31]  Andreas Plückthun,et al.  Docking small ligands in flexible binding sites , 1998 .

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

[33]  Andrew Smellie,et al.  Poling: Promoting conformational variation , 1995, J. Comput. Chem..

[34]  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..

[35]  Sheng-Yong Yang,et al.  Pharmacophore modeling and applications in drug discovery: challenges and recent advances. , 2010, Drug discovery today.

[36]  D. Levitt,et al.  POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. , 1992, Journal of molecular graphics.

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

[38]  Ruben Abagyan,et al.  ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation , 1994, J. Comput. Chem..

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

[40]  M. Congreve,et al.  Fragment-based lead discovery , 2004, Nature Reviews Drug Discovery.

[41]  Alexander D. MacKerell,et al.  Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. , 2011, Current computer-aided drug design.

[42]  Pieter F. W. Stouten,et al.  Fast prediction and visualization of protein binding pockets with PASS , 2000, J. Comput. Aided Mol. Des..

[43]  R. M. Burnett,et al.  DARWIN: A program for docking flexible molecules , 2000, Proteins.

[44]  I. Kuntz,et al.  Molecular docking to ensembles of protein structures. , 1997, Journal of molecular biology.

[45]  W. L. Jorgensen,et al.  AN EXTENDED LINEAR RESPONSE METHOD FOR DETERMINING FREE ENERGIES OF HYDRATION , 1995 .

[46]  S. Kim,et al.  "Soft docking": matching of molecular surface cubes. , 1991, Journal of molecular biology.

[47]  Mika A. Kastenholz,et al.  GRID/CPCA: a new computational tool to design selective ligands. , 2000, Journal of medicinal chemistry.

[48]  E. Shakhnovich,et al.  SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions. , 2002, Journal of medicinal chemistry.

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

[50]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[51]  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.

[52]  Pedro A Fernandes,et al.  Hot spots—A review of the protein–protein interface determinant amino‐acid residues , 2007, Proteins.

[53]  P. Goodford A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[54]  D Fischer,et al.  Surface motifs by a computer vision technique: Searches, detection, and implications for protein–ligand recognition , 1993, Proteins.

[55]  Colin McMartin,et al.  QXP: Powerful, rapid computer algorithms for structure-based drug design , 1997, J. Comput. Aided Mol. Des..

[56]  Janet M. Thornton,et al.  BLEEP—potential of mean force describing protein–ligand interactions: I. Generating potential , 1999 .

[57]  D K Gehlhaar,et al.  De novo design of enzyme inhibitors by Monte Carlo ligand generation. , 1995, Journal of medicinal chemistry.

[58]  H. van de Waterbeemd,et al.  ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.

[59]  J. Thornton,et al.  A method for localizing ligand binding pockets in protein structures , 2005, Proteins.

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

[61]  R L Jernigan,et al.  A preference‐based free‐energy parameterization of enzyme‐inhibitor binding. Applications to HIV‐1‐protease inhibitor design , 1995, Protein science : a publication of the Protein Society.

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

[63]  A J Olson,et al.  Automated docking in crystallography: Analysis of the substrates of aconitase , 1993, Proteins.

[64]  I. Kuntz,et al.  Conformational analysis of flexible ligands in macromolecular receptor sites , 1992 .

[65]  P Burkhard,et al.  An example of a protein ligand found by database mining: description of the docking method and its verification by a 2.3 A X-ray structure of a thrombin-ligand complex. , 1998, Journal of molecular biology.

[66]  Gennady M Verkhivker,et al.  Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. , 1995, Chemistry & biology.

[67]  M. Karplus,et al.  Functionality maps of binding sites: A multiple copy simultaneous search method , 1991, Proteins.

[68]  Hans-Joachim Böhm,et al.  The computer program LUDI: A new method for the de novo design of enzyme inhibitors , 1992, J. Comput. Aided Mol. Des..

[69]  L. Lai,et al.  Towards structure-based protein drug design. , 2011, Biochemical Society transactions.

[70]  Christopher R. Corbeil,et al.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go , 2008, British journal of pharmacology.

[71]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[72]  Peter A. Kollman,et al.  AMBER: Assisted model building with energy refinement. A general program for modeling molecules and their interactions , 1981 .

[73]  I Lasters,et al.  Computation of the binding of fully flexible peptides to proteins with flexible side chains , 1997, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

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

[75]  Kevin P. Clark,et al.  Flexible ligand docking without parameter adjustment across four ligand–receptor complexes , 1995, J. Comput. Chem..

[76]  A. N. Jain,et al.  Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. , 1996, Chemistry & biology.

[77]  H. Wolfson,et al.  Molecular surface recognition by a computer vision-based technique. , 1994, Protein engineering.

[78]  D J Diller,et al.  High throughput docking for library design and library prioritization , 2001, Proteins.

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

[80]  Peter Willett,et al.  Algorithms for the identification of three-dimensional maximal common substructures , 1987, J. Chem. Inf. Comput. Sci..

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

[82]  Robert P. Sheridan,et al.  FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[83]  J. Åqvist,et al.  Ligand binding affinities from MD simulations. , 2002, Accounts of chemical research.

[84]  Aniko Simon,et al.  eHiTS: an innovative approach to the docking and scoring function problems. , 2006, Current protein & peptide science.

[85]  D. Goodsell,et al.  Automated docking of substrates to proteins by simulated annealing , 1990, Proteins.

[86]  Gennady M Verkhivker,et al.  Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. , 1995, Protein engineering.

[87]  Thomas Lengauer,et al.  Multiple automatic base selection: Protein–ligand docking based on incremental construction without manual intervention , 1997, J. Comput. Aided Mol. Des..

[88]  Collin M. Stultz,et al.  The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design , 2009, J. Comput. Aided Mol. Des..

[89]  W. Tong,et al.  Quantitative structure‐activity relationship methods: Perspectives on drug discovery and toxicology , 2003, Environmental toxicology and chemistry.

[90]  De-Xin Kong,et al.  Where is the hope for drug discovery? Let history tell the future. , 2009, Drug discovery today.

[91]  Ajay N. Jain Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search , 2007, J. Comput. Aided Mol. Des..

[92]  Jianling Wang,et al.  The impact of early ADME profiling on drug discovery and development strategy , 2004 .

[93]  Samir Roy,et al.  BHB: A Simple Knowledge-Based Scoring Function to Improve the Efficiency of Database Screening , 2003, J. Chem. Inf. Comput. Sci..

[94]  Z Liu,et al.  Construction of protein binding sites in scaffold structures. , 2000, Biopolymers.

[95]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[96]  G. Klebe,et al.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.

[97]  G. V. Paolini,et al.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..

[98]  Vince Grolmusz,et al.  Evaluating Genetic Algorithms in Protein-Ligand Docking , 2008, ISBRA.

[99]  Richard D. Taylor,et al.  Improved protein–ligand docking using GOLD , 2003, Proteins.

[100]  J. V. Van Drie,et al.  Pharmacophore discovery--lessons learned. , 2003, Current pharmaceutical design.

[101]  L. Kuhn,et al.  Virtual screening with solvation and ligand-induced complementarity , 2000 .