CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions

Smith, Richard; Dunbar, James B.; Ung, Peter M.U.; Esposito, Emilio Xavier; Yang, Chao‐Yie; Wang, Shaomeng; Carlson, Heather A.

doi:10.1021/ci200269q

Cited by 138 publications

(180 citation statements)

References 82 publications

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“…The SIE scoring function parametrized in this manner achieves a reasonable transferability across a wide variety of protein-ligand systems, consistently returning absolute binding affinities within the experimental range, as demonstrated by test cases published in the literature [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. External testing of the standard SIE parametrization in the CSAR-2010 scoring challenge consisting in a curated dataset of 343 protein-ligand complexes diverse with respect to ligands and targets [32,33], afforded binding affinity predictions with a mean-unsigned-error (MUE) of about 2 kcal/mol [34].…”

Section: Introductionmentioning

confidence: 78%

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Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Sulea

Hogues

Purisima

2011

J Comput Aided Mol Des

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We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

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Section: Introductionmentioning

confidence: 78%

“…The most extensive testing of the SIE function was done recently in the Community Structure-Activity Resource (CSAR) scoring challenge consisting of high-resolution cocrystal structures for 343 protein-ligand complexes with high-quality binding affinity data and high diversity with respect to protein targets [32][33][34]. While the dataset Fig.…”

Section: Csarmentioning

confidence: 99%

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Sulea

Hogues

Purisima

2011

J Comput Aided Mol Des

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“…The accuracy of a scoring function can be evaluated in accordance with its applications in the structure-based drug design and discovery [9,[11][12][13][14]. The first one refers to the ability to rank the computationally generated poses, and identify the best-scored one as the ''native'' binding pose, which should be close to what is observed experimentally.…”

Section: Introductionmentioning

confidence: 99%

Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks

Yan

Wang

2016

J Comput Aided Mol Des

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Scoring functions of protein-ligand interactions are widely used in computationally docking software and structure-based drug discovery. Accurate prediction of the binding energy between the protein and the ligand is the main task of the scoring function. The accuracy of a scoring function is normally evaluated by testing it on the benchmarks of protein-ligand complexes. In this work, we report the evaluation analysis of an improved version of scoring function SPecificity and Affinity (SPA). By testing on two independent benchmarks Community Structure-Activity Resource (CSAR) 2014 and Comparative Assessment of Scoring Functions (CASF) 2013, the assessment shows that SPA is relatively more accurate than other compared scoring functions in predicting the interactions between the protein and the ligand. We conclude that the inclusion of the specificity in the optimization can effectively suppress the competitive state on the funnel-like binding energy landscape, and make SPA more accurate in identifying the "native" conformation and scoring the binding decoys. The evaluation of SPA highlights the importance of binding specificity in improving the accuracy of the scoring functions.

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confidence: 99%

“…One important advantage of these computational methods is that they are free of experimental difficulty; thus, the computational methods are expected to reduce the experimental effort involved in the SBDD. The in silico SBDD methods can be categorized into two groups: virtual screening [3][4][5][6][7][8] and de novo drug design. [9][10][11] In virtual screening method, drug candidates are selected from libraries of chemical compounds by predicting their binding free energies approximately.…”

mentioning

confidence: 99%

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

Yamashita

Ueda

Mitsui

et al. 2015

CHEMICAL & PHARMACEUTICAL BULLETIN

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In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.Key words computational drug design; molecular dynamics; binding free energy; high-performance computing; force field parameter As predicted by the Moore's law, 1) computational power progresses exponentially each year. The K computer (RIKEN, Japan) was the first one to reach the computational speed of 10 petaflops (PF).2) Subsequently, the United States and China released 10 PF-scale supercomputers. Thus, such huge computational power is expected to be utilized effectively for progress in a wide variety of science and technology fields. The ultimate purpose of this study is to develop and demonstrate an efficient drug design method with the help of such a stateof-the-art supercomputer.To overcome the difficulty of drug development, many computational structure-based drug design (SBDD) methods have been proposed in the last two decades. One important advantage of these computational methods is that they are free of experimental difficulty; thus, the computational methods are expected to reduce the experimental effort involved in the SBDD. The in silico SBDD methods can be categorized into two groups: virtual screening [3][4][5][6][7][8] and de novo drug design. 9-11)In virtual screening method, drug candidates are selected from libraries of chemical compounds by predicting their binding free energies approximately. In de novo drug de...

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CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions

Cited by 138 publications

References 82 publications

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

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