Abstract
The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol–water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p\({K}_{{\rm a}}\) Challenge, which asked participants to predict p\({K}_{{\rm a}}\) values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol–water log P predictions in SAMPL6 Challenge was higher than cyclohexane–water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol–water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.
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Code and data availability
All SAMPL6 log P challenge instructions, submissions, experimental data and analysis are available at https://github.com/samplchallenges/SAMPL6/tree/master/physical_properties/logP. An archive copy of SAMPL6 GitHub Repository log P challenge directory is also available in the Supplementary Documents bundle (Electronic Supplementary Material 2). Some useful files from this repository are highlighted below. (a) Table of participants and their submission filenames: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/predictions/SAMPL6-user-map-logP.csv. (b) Table of methods including submission IDs, method names, participant assigned method category, and reassigned method categories: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/predictions/SAMPL6-logP-method-map.csv. (c) Submission files of prediction sets: https://github.com/samplchallenges/SAMPL6/tree/master/physical_properties/logP/predictions/submission_files. (d) Python analysis scripts and outputs: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/analysis_with_reassigned_categories/. (e) Table of performance statistics calculated for all methods: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/analysis_with_reassigned_categories/analysis_outputs_withrefs/StatisticsTables/statistics.csv.
Abbreviations
- SAMPL:
-
Statistical Assessment of the Modeling of Proteins and Ligands
- log P :
-
\(\hbox {log}_{10}\) of the organic solvent-water partition coefficient (\(K_{ow}\)) of neutral species
- log D :
-
\(\hbox {log}_{10}\) of organic solvent-water distribution coefficient (\(D_{ow}\))
- p\({K}_{{\rm a}}\) :
-
\(-\hbox {log}_{10}\) of the acid dissociation equilibrium constant
- SEM:
-
Standard error of the mean
- RMSE:
-
Root mean squared error
- MAE:
-
Mean absolute error
- \(\tau\) :
-
Kendall’s rank correlation coefficient (Tau)
- R2 :
-
Coefficient of determination (R\(^{2}\))
- QM:
-
Quantum mechanics
- MM:
-
Molecular mechanics
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Acknowledgements
We would like to thank OpenEye, especially Gaetano Calabró, for help with Orion, and for constructing the Orion workflows partially utilized here. We would like to thank experimental collaborators Timothy Rhodes (ORCID: 0000-0001-7534-9221), Dorothy Levorse, and Brad Sherborne (ORCID: 0000-0002-0037-3427). MI and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges partial support from NIH Grant P30 CA008748. MI, TDB, JDC, and DLM gratefully acknowledge support from NIH Grant R01GM124270 supporting the SAMPL Blind challenges. MI acknowledges support from a Doris J. Hutchinson Fellowship during the collection of experimental data. TDB acknowledges support from the ACM SIGHPC/Intel Fellowship. DLM appreciates financial support from the National Institutes of Health (1R01GM108889-01) and the National Science Foundation (CHE 1352608). We acknowledge contributions from Caitlin Bannan who provided feedback on experimental data collection and structure of log P challenge from a computational chemist’s perspective. MI and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. TF thanks BioByte, MOE, and Molecular Discovery for allowing us the include log P predictions calculated by their software in this work as empirical reference calculations.
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Conceptualization, MI, JDC, DLM ; Methodology, MI, TDB, DM, JDC ; Software, MI, TDB, AR ; Formal Analysis, MI, TDB ; Investigation, MI, TDB, DLM, TF; Resources, JDC, DLM; Data Curation, MI, TDB ; Writing-Original Draft, MI, TDB, DLM, TF; Writing - Review and Editing, MI, TDB, DLM, TF, JDC, AZ; Visualization, MI, TDB ; Supervision, DLM, JDC ; Project Administration, MI ; Funding Acquisition, DLM, JDC, MI, TDB.
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JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software, and DLM is an Open Science Fellow with Silicon Therapeutics. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Vir Biotechnology, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, The Einstein Foundation, and the Sloan Kettering Institute. A complete list of funding can be found at http://choderalab.org/funding.
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Işık, M., Bergazin, T.D., Fox, T. et al. Assessing the accuracy of octanol–water partition coefficient predictions in the SAMPL6 Part II log P Challenge. J Comput Aided Mol Des 34, 335–370 (2020). https://doi.org/10.1007/s10822-020-00295-0
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DOI: https://doi.org/10.1007/s10822-020-00295-0