SMoG:  de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence

DeWitte, Robert S.; Shakhnovich, Eugene I.

doi:10.1021/ja960751u

Cited by 249 publications

(201 citation statements)

References 29 publications

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“…Empirical Knowledge-Based DOCK [32] AutoDock [34] SMoG [82] AutoDock [34] GlideScore [37] DrugScore [62] GoldScore [35] ChemScore [60] PMF_Score [83] ICM [46] X_Score [66] MotifScore [84] LigandFit [47] F_Score [73] RF_Score [85] Molegro Virtual Docker [48] Fresno [75] PESD_SVM [86] SYBYL_G-Score [73] SCORE [76] PoseScore [87] SYBYL_D-Score [73] LUDI [77] MedusaScore [74] SFCscore [78] HYDE [79] LigScore [80] PLP [81] …”

Section: Force-field-basedmentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure-and ligand-based methods.

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Section: Force-field-basedmentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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“…5, 548-553 Grzybowski et al reported an in silico combinatorial method (CombiSMoG 554,555 ) that employs simulation to generate a virtual library of inhibitors and then to rate the candidate inhibitors by their binding free energies generated using knowledge-based potential functions. 189 HCA II was used as the model enzyme for this study for several reasons: (i) The location of the benzenesulfonamide group could be specified.…”

Section: Computational Approaches To Ligandmentioning

confidence: 99%

Carbonic Anhydrase as a Model for Biophysical and Physical-Organic Studies of Proteins and Protein−Ligand Binding

et al. 2008

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I. Introduction to Carbonic Anhydrase (CA) and to the Review 948 1. Introduction: Overview of CA as a Model 948 1.1. Value of Models 950 1.2. Objectives and Scope of the Review 950 2. Overview of Enzymatic Activity 950 3. Medical Relevance 951 II. Structure and Structure−Function Relationships of CA 953 4. Global and Active-Site Structure 953 4.1. Structure of Isoforms 953 4.2. Isolation and Purification 954 4.3. Crystallization 954 4.4. Structures Determined by X-ray Crystallography and NMR 955 4.4.1. Structures Determined by X-ray Crystallography 955 4.4.2. Structure Determined by NMR 955 4.5. Global Structural Features 963 4.6. Structure of the Binding Cavity 964 4.7. Zn II -Bound Water 965 5. Metalloenzyme Variants 966 6. Structure−Function Relationships in the Catalytic Active Site of CA 968 6.1. Effects of Ligands Directly Bound to Zn II 968 6.2. Effects of Indirect Ligands 969 7. Physical-Organic Models of the Active Site of CA 969 III. Using CA as a Model to Study Protein−Ligand Binding 970 8. Assays for Measuring Thermodynamic and Kinetic Parameters for Binding of Substrates and Inhibitors 970 8.1. Overview 970 8.

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“…The observed frequency distributions are converted to what is usually referred to as potentials of mean force or knowledge-based potentials. Several such potentials to predict binding affinity have been developed (e.g., PMF [22], DrugScore [23], SmoG [24], Bleep [25]). All these approaches differ mainly in the size of the training database that was employed and in the types of molecular interaction that were considered.…”

Section: Scoring or Affinity Predictionmentioning

confidence: 99%

Structure-Based Drug Design: Docking and Scoring

Kroemer

2007

CPPS

280

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This review gives an introduction into ligand -receptor docking and illustrates the basic underlying concepts. An overview of different approaches and algorithms is provided. Although the application of docking and scoring has led to some remarkable successes, there are still some major challenges ahead, which are outlined here as well. Approaches to address some of these challenges and the latest developments in the area are presented. Some aspects of the assessment of docking program performance are discussed. A number of successful applications of structure-based virtual screening are described.

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SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence

Cited by 249 publications

References 29 publications

Molecular Docking and Structure-Based Drug Design Strategies

Molecular Docking and Structure-Based Drug Design Strategies

Carbonic Anhydrase as a Model for Biophysical and Physical-Organic Studies of Proteins and Protein−Ligand Binding

Structure-Based Drug Design: Docking and Scoring

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