Can we use docking and scoring for hit-to-lead optimization?

Enyedy, Istvan; Egan, William J.

doi:10.1007/s10822-007-9165-4

Cited by 88 publications

(74 citation statements)

References 49 publications

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“…(2) Virtual screening. Based on multiple reports in this issue, we recommend reporting the area under the curve for ROC plots (AUC) [1,3,[7][8][9][10]. These have for a long time been a standard metric for other fields and for good reasons.…”

Section: Recommendations For Reporting Resultsmentioning

confidence: 99%

“…Chemical similarity is assumed, thus placing this technique in the domain of lead optimization, not lead discovery. As illustrated in at least one of the reports here [3], such methods are not currently successful when properly considered with control computations that include, for example, correlations of affinity with molecular weight.…”

Section: Dockingmentioning

confidence: 99%

“…(5) Parameter tuning. Many papers in this issue show how the choice of parameters influences the apparent quality of results [3,4,9]. There is a dichotomy of opinion on whether ''tuned'' performance figures are relevant to future application of a method when the correct answer is unknown.…”

Section: Dockingmentioning

confidence: 99%

“…As it is, the best generally hoped for is a rough correlation between activity and score and even in these cases there are obvious, and not so obvious, pitfalls. However, a rough correlation between activity and score is frequently obtained simply by equating activity with, for example, a monotonic function of molecular weight [3]. (4) General.…”

Section: The Issuesmentioning

confidence: 99%

“…This special issue of the Journal of Computer-Aided Molecular Design includes eleven papers, each of which makes a detailed study of at least one aspect of methodological evaluation [1][2][3][4][5][6][7][8][9][10][11]. The papers collected within this issue make the detailed case for the recommendations that follow; the recommendations are intended to provide guidance to editorial boards and reviewers of work submitted for publication in our field.…”

Section: Introductionmentioning

confidence: 99%

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Recommendations for evaluation of computational methods

Jain

Nicholls

2008

J Comput Aided Mol Des

329

300

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The field of computational chemistry, particularly as applied to drug design, has become increasingly important in terms of the practical application of predictive modeling to pharmaceutical research and development. Tools for exploiting protein structures or sets of ligands known to bind particular targets can be used for bindingmode prediction, virtual screening, and prediction of activity. A serious weakness within the field is a lack of standards with respect to quantitative evaluation of methods, data set preparation, and data set sharing. Our goal should be to report new methods or comparative evaluations of methods in a manner that supports decision making for practical applications. Here we propose a modest beginning, with recommendations for requirements on statistical reporting, requirements for data sharing, and best practices for benchmark preparation and usage.

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Section: Recommendations For Reporting Resultsmentioning

confidence: 99%

Section: Dockingmentioning

confidence: 99%

Section: Dockingmentioning

confidence: 99%

Section: The Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recommendations for evaluation of computational methods

Jain

Nicholls

2008

J Comput Aided Mol Des

329

300

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The Challenge of Affinity Prediction: Scoring Functions for Structure‐Based Virtual Screening

Sotriffer¹,

Matter²

2011

Methods and Principles in Medicinal Chemistry

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The purpose of virtual screening is to provide hits with novel chemical structure as a starting point for lead optimization after careful assessment of their optimization potential [1].Structure-based virtual screening works on the premise of shape and interaction complementarity of the ligand with its putative target binding site, for which experimental 3D structures or validated homology models are available. Using docking algorithms combined in various ways with other filters (e.g., drug-likeness, physiochemical properties, binding site constraints, pharmacophore models, and molecular similarity), compounds are selected that have a high likelihood to interact with the target. To this end, the first step is to generate a proposed binding conformation (pose) using validated docking engines. These poses need to be sorted to identify the conformation that is closest to an experimental binding pose. Subsequently, the free energy of binding (DG ) for a particular conformation of a single compound interacting with the protein has to be estimated to rank the ligands and select those with the highest chance for biological activity. Approaches providing such estimates of affinity are, thus, of utmost importance in virtual screening. Normally, this pose ranking and affinity estimation is carried out with the help of scoring functions. Scoring functions are approximate mathematical models for predicting the strength of the noncovalent interaction between two binding partners. Such functions also guide the conformational and orientational search of the ligand in the binding cavity during the docking process.This chapter focuses on different aspects of scoring functions for affinity prediction and pose ranking. First, we will outline the physicochemical background of current scoring approaches. As different scoring functions were recently reviewed [2-4] and their performance was extensively compared [5-8], we will provide only a brief overview of the different scoring function classes and focus on unique features and applications. Hence, we outline novel strategies and computational tools with an impact on improving the success rate in scoring protein-ligand interactions.

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Scoring Functions for Protein–Ligand Interactions

Sotriffer¹

2012

Protein‐Ligand Interactions

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Can we use docking and scoring for hit-to-lead optimization?

Cited by 88 publications

References 49 publications

Recommendations for evaluation of computational methods

Recommendations for evaluation of computational methods

The Challenge of Affinity Prediction: Scoring Functions for Structure‐Based Virtual Screening

Scoring Functions for Protein–Ligand Interactions

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