Overview of the SAMPL6 host–guest binding affinity prediction challenge
Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accura...
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| Published in | Journal of computer-aided molecular design Vol. 32; no. 10; pp. 937 - 963 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.10.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-654X 1573-4951 1573-4951 |
| DOI | 10.1007/s10822-018-0170-6 |
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| Abstract | Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host–guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host–guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host–guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host–guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host–guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy. |
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| AbstractList | Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host–guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host–guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host–guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host–guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host–guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy. Accurately predicting the binding affinities of small organic molecules to biological macro-molecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macro-molecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[ 8 ]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states) may be required to further enhance predictive accuracy. Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy. |
| Author | Chiu, Michael W. Gibb, Bruce C. Gilson, Michael K. Isaacs, Lyle Chodera, John D. Mobley, David L. Yao, Wei Rizzi, Andrea Murkli, Steven McNeill, John N. Sullivan, Matthew |
| AuthorAffiliation | 4 Department of Chemistry, Tulane University, Louisiana, LA 70118, USA 3 Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA 6 Qualcomm Institute, University of California, San Diego, La Jolla, CA 92093, USA 1 Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA 5 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA 7 Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California 92697, USA 2 Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA |
| AuthorAffiliation_xml | – name: 1 Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA – name: 7 Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California 92697, USA – name: 3 Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA – name: 4 Department of Chemistry, Tulane University, Louisiana, LA 70118, USA – name: 5 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA – name: 2 Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA – name: 6 Qualcomm Institute, University of California, San Diego, La Jolla, CA 92093, USA |
| Author_xml | – sequence: 1 givenname: Andrea orcidid: 0000-0001-7693-2013 surname: Rizzi fullname: Rizzi, Andrea organization: Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Tri-Institutional Training Program in Computational Biology and Medicine – sequence: 2 givenname: Steven orcidid: 0000-0003-1563-0139 surname: Murkli fullname: Murkli, Steven organization: Department of Chemistry and Biochemistry, University of Maryland – sequence: 3 givenname: John N. orcidid: 0000-0002-8401-2388 surname: McNeill fullname: McNeill, John N. organization: Department of Chemistry and Biochemistry, University of Maryland – sequence: 4 givenname: Wei orcidid: 0000-0002-4229-0486 surname: Yao fullname: Yao, Wei organization: Department of Chemistry, Tulane University – sequence: 5 givenname: Matthew orcidid: 0000-0002-8112-2848 surname: Sullivan fullname: Sullivan, Matthew organization: Department of Chemistry, Tulane University – sequence: 6 givenname: Michael K. orcidid: 0000-0002-3375-1738 surname: Gilson fullname: Gilson, Michael K. organization: Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California – sequence: 7 givenname: Michael W. surname: Chiu fullname: Chiu, Michael W. organization: Qualcomm Institute, University of California – sequence: 8 givenname: Lyle orcidid: 0000-0002-4079-332X surname: Isaacs fullname: Isaacs, Lyle organization: Department of Chemistry and Biochemistry, University of Maryland – sequence: 9 givenname: Bruce C. orcidid: 0000-0002-4478-4084 surname: Gibb fullname: Gibb, Bruce C. organization: Department of Chemistry, Tulane University – sequence: 10 givenname: David L. orcidid: 0000-0002-1083-5533 surname: Mobley fullname: Mobley, David L. email: dmobley@uci.edu organization: Department of Pharmaceutical Sciences and Department of Chemistry, University of California – sequence: 11 givenname: John D. orcidid: 0000-0003-0542-119X surname: Chodera fullname: Chodera, John D. email: john.chodera@choderalab.org organization: Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30415285$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Springer Nature Switzerland AG 2018 Journal of Computer-Aided Molecular Design is a copyright of Springer, (2018). All Rights Reserved. |
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| DOI | 10.1007/s10822-018-0170-6 |
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| Issue | 10 |
| Keywords | SAMPL6 Octa-acid Blind challenge Binding affinity Host–guest Free energy Cucurbituril |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceptualization, AR, JDC, DLM; Methodology, AR, JDC, DLM; Software, AR; Formal Analysis, AR, JDC; Investigation, AR, QY, SM, MS, JNM; Resources, JDC, BCG, LI, MWC, MKG, DLM; Data Curation, AR, MWC; Writing-Original Draft, AR, JDC; Writing - Review and Editing, AR, JDC, DLM, MKG, LI, BCG, SM; Visualization, AR, SM; Supervision, JDC, DLM; Project Administration, AR, JDC, DLM; Funding Acquisition, JDC, DLM, MKG, BCG, LI. Author Contributions |
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| PublicationSubtitle | Incorporating Perspectives in Drug Discovery and Design |
| PublicationTitle | Journal of computer-aided molecular design |
| PublicationTitleAbbrev | J Comput Aided Mol Des |
| PublicationTitleAlternate | J Comput Aided Mol Des |
| PublicationYear | 2018 |
| Publisher | Springer International Publishing Springer Nature B.V |
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| SubjectTerms | Accuracy Affinity Animal Anatomy Binding Bridged-Ring Compounds - chemistry Carboxylic Acids - chemistry Chemical effects Chemical treatment Chemistry Chemistry and Materials Science Computer Applications in Chemistry Computer Simulation Cycloparaffins - chemistry Dependence Drug Design Electronic structure First principles Free energy Histology Identification methods Imidazoles - chemistry Ligands Machine learning Macrocyclic Compounds - chemistry Macromolecules Mathematical models Molecular Structure Morphology Organic chemistry Performance prediction Physical Chemistry Protein Binding Proteins - chemistry Protonation Selectivity Software Solvents Statistical correlation System effectiveness Systematic errors Thermodynamics |
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| Title | Overview of the SAMPL6 host–guest binding affinity prediction challenge |
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