1
|
Draper MR, Waterman A, Dannatt JE, Patel P. Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge. Phys Chem Chem Phys 2024; 26:7907-7919. [PMID: 38376855 PMCID: PMC10938873 DOI: 10.1039/d3cp04140a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.
Collapse
Affiliation(s)
- Michael R Draper
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | - Asa Waterman
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | | | - Prajay Patel
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| |
Collapse
|
2
|
Muralisankar M, Chen JR, Haribabu J, Ke SC. Effective and Selective Ru(II)-Arene Complexes Containing 4,4'-Substituted 2,2' Bipyridine Ligands Targeting Human Urinary Bladder Cancer Cells. Int J Mol Sci 2023; 24:11896. [PMID: 37569273 PMCID: PMC10418970 DOI: 10.3390/ijms241511896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
Cisplatin-based chemotherapy is a common regimen for bladder cancer, a life-threatening cancer with more than 500,000 new cases worldwide annually. Like many other metallodrugs, cisplatin causes severe side effects for its general toxicity. Organoruthenium is known for its structural stability, good anticancer activity, and possible low general toxicity. Here, we have prepared and characterized a series of water-soluble ruthenium-arene complexes with N,N'-chelating ligands: [Ru(II)-η6-arene-(4,4'-(X)2-2,2'-bipyridine)Cl]Cl (arene = p-cymene, X = C4H9 (1), COOH (2), COOCH3 (3), COOC2H5 (4); arene = benzene, X = C4H9 (5), COOCH3 (6), COOC2H5 (7)). These complexes are carefully characterized using single-crystal X-ray diffraction, UV-vis, IR, 1H NMR, and MALDI-TOF MS spectroscopy. Their DFT-calculated structural and thermodynamic properties are consistent with the experimental observations. Biophysicochemical studies of complex interaction with CTDNA and BSA supported by molecular docking simulations reveal suitable properties of 1-7 as anticancer agents. Cytotoxicities of 1-7 are evaluated on healthy human MCF-10a-breast epithelial and African green monkey Vero cells, and carcinoma human HepG-2-hepatic, T24-bladder, and EAhy-926-endothelial cells. All complexes exhibit much higher cytotoxicity for T24 than cisplatin. Particularly, 1 and 2 are also highly selective toward T24. Fluorescence imaging and flow cytometry demonstrate that 1 and 2 penetrate T24 cell membrane and induce early apoptosis at their respective IC50 concentrations, which ultimately lead to cell death. Statistical analysis suggests that the order of importance for T24 cell antiproliferation is protein binding, Log p, Ru-Cl bond length, while DNA binding is the least important. This study is the first to report the anti-bladder cancer efficacy of Ru-arene-2,2'-bipyridine complexes, and may provide insights for rational design of organoruthenium drugs in the enduring search for new chemotherapeutic agents.
Collapse
Affiliation(s)
- Mathiyan Muralisankar
- Physics Department, National Dong Hwa University, Hualien 97401, Taiwan; (M.M.); (J.-R.C.)
| | - Jun-Ru Chen
- Physics Department, National Dong Hwa University, Hualien 97401, Taiwan; (M.M.); (J.-R.C.)
| | - Jebiti Haribabu
- Facultad de Medicina, Universidad de Atacama, Copayapu 485, Copiapo 1531772, Chile;
| | - Shyue-Chu Ke
- Physics Department, National Dong Hwa University, Hualien 97401, Taiwan; (M.M.); (J.-R.C.)
| |
Collapse
|
3
|
Rodriguez SA, Tran JV, Sabatino SJ, Paluch AS. Predicting octanol/water partition coefficients and pKa for the SAMPL7 challenge using the SM12, SM8 and SMD solvation models. J Comput Aided Mol Des 2022; 36:687-705. [PMID: 36117236 DOI: 10.1007/s10822-022-00474-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/29/2022] [Indexed: 11/29/2022]
Abstract
Blind predictions of octanol/water partition coefficients and pKa at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. Octanol/water partition coefficients were predicted from solvation free energies computed using electronic structure calculations with the SM12, SM8 and SMD solvation models. Within these calculations we compared the use of gas- and solution-phase optimized geometries of the solute. Based on these calculations we found that in general the use of solution phase-optimized geometries increases the affinity of the solutes for water as compared to octanol, with the use of gas-phase optimized geometries resulting in the better agreement with experiment. The pKa is computed using the direct approach, scaled solvent-accessible surface model, and the inclusion of an explicit water molecule, where the latter two methods have previously been shown to offer improved predictions as compared to the direct approach. We find that the use of an explicit water molecule provides superior predictions, and that the predicted macroscopic pKa is sensitive to the employed microstates.
Collapse
Affiliation(s)
- Sergio A Rodriguez
- Instituto de Ciencias Químicas, Facultad de Agronomía y Agroindustrias, Universidad Nacional de Santiago del Estero, CONICET, Santiago del Estero, Argentina
| | - Jasmine Vy Tran
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA
| | - Spencer J Sabatino
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA
| | - Andrew S Paluch
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA.
| |
Collapse
|
4
|
Falcioni F, Kalayan J, Henchman RH. Energy-entropy prediction of octanol-water logP of SAMPL7 N-acyl sulfonamide bioisosters. J Comput Aided Mol Des 2021; 35:831-840. [PMID: 34244906 PMCID: PMC8295089 DOI: 10.1007/s10822-021-00401-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/17/2021] [Indexed: 12/23/2022]
Abstract
Partition coefficients quantify a molecule's distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol-water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute.
Collapse
Affiliation(s)
- Fabio Falcioni
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
| | - Jas Kalayan
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Richard H Henchman
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
- Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
| |
Collapse
|
5
|
Donyapour N, Dickson A. Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method. J Comput Aided Mol Des 2021; 35:819-830. [PMID: 34181200 PMCID: PMC8295205 DOI: 10.1007/s10822-021-00400-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/17/2021] [Indexed: 02/02/2023]
Abstract
The prediction of [Formula: see text] values is one part of the statistical assessment of the modeling of proteins and ligands (SAMPL) blind challenges. Here, we use a molecular graph representation method called Geometric Scattering for Graphs (GSG) to transform atomic attributes to molecular features. The atomic attributes used here are parameters from classical molecular force fields including partial charges and Lennard-Jones interaction parameters. The molecular features from GSG are used as inputs to neural networks that are trained using a "master" dataset comprised of over 41,000 unique [Formula: see text] values. The specific molecular targets in the SAMPL7 [Formula: see text] prediction challenge were unique in that they all contained a sulfonyl moeity. This motivated a set of ClassicalGSG submissions where predictors were trained on different subsets of the master dataset that are filtered according to chemical types and/or the presence of the sulfonyl moeity. We find that our ranked prediction obtained 5th place with an RMSE of 0.77 [Formula: see text] units and an MAE of 0.62, while one of our non-ranked predictions achieved first place among all submissions with an RMSE of 0.55 and an MAE of 0.44. After the conclusion of the challenge we also examined the performance of open-source force field parameters that allow for an end-to-end [Formula: see text] predictor model: General AMBER Force Field (GAFF), Universal Force Field (UFF), Merck Molecular Force Field 94 (MMFF94) and Ghemical. We find that ClassicalGSG models trained with atomic attributes from MMFF94 can yield more accurate predictions compared to those trained with CGenFF atomic attributes.
Collapse
Affiliation(s)
- Nazanin Donyapour
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Alex Dickson
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA.
| |
Collapse
|
6
|
SAMPL7 blind challenge: quantum-mechanical prediction of partition coefficients and acid dissociation constants for small drug-like molecules. J Comput Aided Mol Des 2021; 35:841-851. [PMID: 34164769 DOI: 10.1007/s10822-021-00402-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/17/2021] [Indexed: 02/02/2023]
Abstract
The physicochemical properties of a drug molecule determine the therapeutic effectiveness of the drug. Thus, the development of fast and accurate theoretical approaches for the prediction of such properties is inevitable. The participation to the SAMPL7 challenge is based on the estimation of logP coefficients and pKa values of small drug-like sulfonamide derivatives. Thereby, quantum mechanical calculations were carried out in order to calculate the free energy of solvation and the transfer energy of 22 drug-like compounds in different environments (water and n-octanol) by employing the SMD solvation model. For logP calculations, we studied eleven different methodologies to calculate the transfer free energies, the lowest RMSE value was obtained for the M06L/def2-TZVP//M06L/def2-SVP level of theory. On the other hand, we employed an isodesmic reaction scheme within the macro pKa framework; this was based on selecting reference molecules similar to the SAMPL7 challenge molecules. Consequently, highly well correlated pKa values were obtained with the M062X/6-311+G(2df,2p)//M052X/6-31+G(d,p) level of theory.
Collapse
|
7
|
Bergazin TD, Tielker N, Zhang Y, Mao J, Gunner MR, Francisco K, Ballatore C, Kast SM, Mobley DL. Evaluation of log P, pK a, and log D predictions from the SAMPL7 blind challenge. J Comput Aided Mol Des 2021; 35:771-802. [PMID: 34169394 PMCID: PMC8224998 DOI: 10.1007/s10822-021-00397-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/05/2021] [Indexed: 12/16/2022]
Abstract
The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.
Collapse
Affiliation(s)
| | - Nicolas Tielker
- Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany
| | - Yingying Zhang
- Department of Physics, The Graduate Center, City University of New York, New York, 10016, USA
| | - Junjun Mao
- Department of Physics, City College of New York, New York, 10031, USA
| | - M R Gunner
- Department of Physics, The Graduate Center, City University of New York, New York, 10016, USA.,Department of Physics, City College of New York, New York, 10031, USA
| | - Karol Francisco
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Ja Jolla, CA, 92093-0756, USA
| | - Carlo Ballatore
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Ja Jolla, CA, 92093-0756, USA
| | - Stefan M Kast
- Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA. .,Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
| |
Collapse
|
8
|
Işık M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL. Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge. J Comput Aided Mol Des 2020; 34:335-370. [PMID: 32107702 PMCID: PMC7138020 DOI: 10.1007/s10822-020-00295-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
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[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] 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.
Collapse
Affiliation(s)
- Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA.
| | | | - Thomas Fox
- Computational Chemistry, Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach, Germany
| | - Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
| |
Collapse
|