Current topics in computer-aided drug design.

The addition of computer-aided drug design (CADD) technologies to the research and drug discovery approaches could lead to a reduction of up to 50% in the cost of drug design. Designing a drug is the process of finding or creating a molecule which has a specific activity on a biological organism. Development and drug discovery is a time-consuming, expensive, and interdisciplinary process whereas scientific advancements during the past two decades have altered the way pharmaceutical research produces new bioactive molecules. Advances in computational techniques and hardware solutions have enabled in silico methods to speed up lead optimization and identification. We will review current topics in computer-aided molecular design underscoring some of the most recent approaches and interdisciplinary processes. We will discuss some of the most efficient pathways and design.

[1]  Theodora M. Steindl,et al.  Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein. , 2005, Journal of medicinal chemistry.

[2]  F. Sanz,et al.  Anchor-GRIND: filling the gap between standard 3D QSAR and the GRid-INdependent descriptors. , 2005 .

[3]  Thomas Lengauer,et al.  Novel technologies for virtual screening. , 2004, Drug discovery today.

[4]  R. Lavery,et al.  ARTIST: An activated method in internal coordinate space for sampling protein energy landscapes , 2006, Proteins.

[5]  K. Stewart,et al.  Drug Guru: a computer software program for drug design using medicinal chemistry rules. , 2006, Bioorganic & medicinal chemistry.

[6]  T Langer,et al.  Lead optimization Pharmacophore definition and 3 D searches , 2005 .

[7]  Christopher W. Murray,et al.  The sensitivity of the results of molecular docking to induced fit effects: Application to thrombin, thermolysin and neuraminidase , 1999, J. Comput. Aided Mol. Des..

[8]  Carlton A Taft,et al.  Rational design of novel diketoacid-containing ferrocene inhibitors of HIV-1 integrase. , 2005, Bioorganic chemistry.

[9]  J. Wu,et al.  Design, synthesis, and antiviral activity of adenosine 5'-phosphonate analogues as chain terminators against hepatitis C virus. , 2005, Journal of medicinal chemistry.

[10]  Z R Li,et al.  Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. , 2006, Mini reviews in medicinal chemistry.

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

[12]  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..

[13]  Uko Maran,et al.  Open Computing Grid for Molecular Science and Engineering , 2006, J. Chem. Inf. Model..

[14]  David G. Lloyd,et al.  Permuting input for more effective sampling of 3D conformer space , 2006, J. Comput. Aided Mol. Des..

[15]  Gianni Chessari,et al.  Application of fragment screening by X-ray crystallography to beta-secretase. , 2007, Journal of medicinal chemistry.

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

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

[18]  C. Taft,et al.  ADMET Properties, Database Screening, Molecular Dynamics, Density Functional, and Docking Studies Of Novel Potential Anti-Cancer Compounds , 2006, Journal of biomolecular structure & dynamics.

[19]  Carlton Anthony Taft and Carlos Henrique Tomich de Paula d Silva.  Cancer and Aids: New Trends in Drug Design and Chemotherapy , 2006 .

[20]  Renate Griffith,et al.  Combining structure-based drug design and pharmacophores. , 2005, Journal of molecular graphics & modelling.

[21]  G. Klebe.  Virtual ligand screening: strategies, perspectives and limitations , 2006, Drug Discovery Today.

[22]  Yovani Marrero-Ponce,et al.  Non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-adjacency matrix: a novel approach for computational in silico screening and “rational” selection of new lead antibacterial agents , 2006, Journal of molecular modeling.

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

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

[25]  Ying Xue,et al.  Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents , 2005 .

[26]  C. Taft,et al.  A molecular modeling and QSAR study of suppressors of the growth of Trypanosoma cruzi epimastigotes. , 2004, Journal of molecular graphics & modelling.

[27]  Johann Gasteiger,et al.  Chemoinformatics: a new field with a long tradition , 2006, Analytical and bioanalytical chemistry.

[28]  Ivone Carvalho,et al.  Virtual Screening, Molecular Interaction Field, Molecular Dynamics, Docking, Density Functional, and ADMET Properties of Novel AChE Inhibitors in Alzheimer's Disease , 2007, Journal of biomolecular structure & dynamics.

[29]  Carlton A. Taft,et al.  MOLECULAR DYNAMICS, DOCKING, DENSITY FUNCTIONAL, AND ADMET STUDIES OF HIV-1 REVERSE TRANSCRIPTASE INHIBITORS , 2006 .

[30]  Robert D. Clark,et al.  A marriage made in torsional space: using GALAHAD models to drive pharmacophore multiplet searches , 2007, J. Comput. Aided Mol. Des..

[31]  Hong Huang Lin,et al.  Computer prediction of drug resistance mutations in proteins. , 2005, Drug discovery today.

[32]  Han van de Waterbeemd,et al.  Drug bioavailability : estimation of solubility, permeability, absorption and bioavailability , 2003 .

[33]  R. Powers,et al.  Comparison of protein active site structures for functional annotation of proteins and drug design , 2006, Proteins.

[34]  S. O'Brien,et al.  Greater than the sum of its parts: combining models for useful ADMET prediction. , 2005, Journal of medicinal chemistry.

[35]  P Mátyus,et al.  Application of neural networks in structure–activity relationships , 1999, Medicinal research reviews.

[36]  Peter W Swaan,et al.  Computational approaches to modeling drug transporters. , 2006, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[37]  G. Labesse,et al.  LEA3D: a computer-aided ligand design for structure-based drug design. , 2005, Journal of medicinal chemistry.

[38]  R. Glen,et al.  Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. , 1995, Journal of molecular biology.

[39]  Ingebrigt Sylte,et al.  Molecular modelling of drug targets: the past, the present and the future. , 2005, Basic & clinical pharmacology & toxicology.

[40]  Yoshihisa Inoue,et al.  Propensities of polar and aromatic amino acids in noncanonical interactions: nonbonded contacts analysis of protein-ligand complexes in crystal structures. , 2007, Journal of medicinal chemistry.

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

[42]  Yu Zong Chen,et al.  Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. , 2007, Drug discovery today.

[43]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.

[44]  Yuan-Ping Pang,et al.  From genome to drug lead: Identification of a small-molecule inhibitor of the SARS virus , 2005, Bioorganic & Medicinal Chemistry Letters.

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