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Spiekermann KA, Dong X, Menon A, Green WH, Pfeifle M, Sandfort F, Welz O, Bergeler M. Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks. J Phys Chem A 2024; 128:8384-8403. [PMID: 39298746 DOI: 10.1021/acs.jpca.4c04121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP. Gibbs free energies of activation and of reaction for these radical reactions in 40 common solvents are obtained using COSMO-RS for solvation effects. These balanced reactions involve the elements H, C, N, O, and S, contain up to 19 heavy atoms, and have atom-mapped SMILES. All transition states are verified by an intrinsic reaction coordinate calculation. We next train a deep graph network to directly estimate the Gibbs free energy of activation and of reaction in both gas and solution phases using only the atom-mapped SMILES of the reactant and product and the SMILES of the solvent. This simple input representation avoids computationally expensive optimizations for the reactant, transition state, and product structures during inference, making our model well-suited for high-throughput predictive chemistry and quickly providing information for (retro-)synthesis planning tools. To properly measure model performance, we report results on both interpolative and extrapolative data splits and also compare to several baseline models. During training and testing, the data set is augmented by including the reverse direction of each reaction and variants with different resonance structures. After data augmentation, we have around 2 million entries to train the model, which achieves a testing set mean absolute error of 1.16 kcal mol-1 for the Gibbs free energy of activation in solution. We anticipate this model will accelerate predictions for high-throughput screening to quickly identify relevant reactions in solution, and our data set will serve as a benchmark for future studies.
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Affiliation(s)
- Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Angiras Menon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Mark Pfeifle
- BASF Digital Solutions GmbH, Ludwigshafen am Rhein 67061, Germany
| | - Frederik Sandfort
- BASF SE, Scientific Modeling, Group Research, Ludwigshafen am Rhein 67056, Germany
| | - Oliver Welz
- BASF SE, Scientific Modeling, Group Research, Ludwigshafen am Rhein 67056, Germany
| | - Maike Bergeler
- BASF SE, Scientific Modeling, Group Research, Ludwigshafen am Rhein 67056, Germany
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2
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Johnson MS, Mueller JN, Daniels C, Najm HN, Zádor J. Diffusion-Limited Kinetics in Reactive Systems. J Phys Chem A 2024. [PMID: 38670062 DOI: 10.1021/acs.jpca.4c00727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
A proper representation of chemical kinetics is vital to understanding, modeling, and optimizing many important chemical processes. In liquid and surface phases, where diffusion is slow, the rate at which the reactants diffuse together limits the overall rate of many elementary reactions. Commonly, the textbook Smoluchowski theory is utilized to estimate effective rate coefficients in the liquid phase. On surfaces, modelers commonly resort to much more complex and expensive Kinetic Monte Carlo (KMC) simulations. Here, we extend the Smoluchowski model to allow the diffusing species to undergo chemical reactions and derive analytical formulas for the diffusion-limited rate coefficients for 3D, 2D, and 2D/3D interface cases. With these equations, we are able to demonstrate that when species react faster than they diffuse they can react orders of magnitude faster than predicted by Smoluchowski theory, through what we term "the reactive transport effect". We validate the derived steady-state equations against particle Monte Carlo (PMC) simulations, KMC simulations, and non-steady-state solutions. Furthermore, using PMC and KMC simulations, we propose corrections that agree with all limits and the computed data for the 2D and 2D/3D interface steady-state equations, accounting for unique limitations in the associated derived equations. Additionally, we derive equations to handle couplings between diffusion-limited rate coefficients in reaction networks. We believe these equations should make it possible to run much more accurate mean-field simulations of liquids, surfaces, and liquid-surface interfaces accounting for diffusion limitations and the reactive transport effect.
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Affiliation(s)
- Matthew S Johnson
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Joy N Mueller
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Craig Daniels
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
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3
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Pang HW, Dong X, Johnson MS, Green WH. Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation. J Phys Chem A 2024; 128:2891-2907. [PMID: 38536892 DOI: 10.1021/acs.jpca.4c00569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.
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Affiliation(s)
- Hao-Wei Pang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Matthew S Johnson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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4
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Comanescu C, Racovita RC. An Overview of Degradation Strategies for Amitriptyline. Int J Mol Sci 2024; 25:3822. [PMID: 38612638 PMCID: PMC11012176 DOI: 10.3390/ijms25073822] [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: 01/01/2024] [Revised: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
Antidepressant drugs play a crucial role in the treatment of mental health disorders, but their efficacy and safety can be compromised by drug degradation. Recent reports point to several drugs found in concentrations ranging from the limit of detection (LOD) to hundreds of ng/L in wastewater plants around the globe; hence, antidepressants can be considered emerging pollutants with potential consequences for human health and wellbeing. Understanding and implementing effective degradation strategies are essential not only to ensure the stability and potency of these medications but also for their safe disposal in line with current environment remediation goals. This review provides an overview of degradation pathways for amitriptyline, a typical tricyclic antidepressant drug, by exploring chemical routes such as oxidation, hydrolysis, and photodegradation. Connex issues such as stability-enhancing approaches through formulation and packaging considerations, regulatory guidelines, and quality control measures are also briefly noted. Specific case studies of amitriptyline degradation pathways forecast the future perspectives and challenges in this field, helping researchers and pharmaceutical manufacturers to provide guidelines for the most effective degradation pathways employed for minimal environmental impact.
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Affiliation(s)
- Cezar Comanescu
- Department of Inorganic Chemistry, Physical Chemistry and Electrochemistry, Faculty of Chemical Engineering and Biotechnologies, National University of Science and Technology POLITEHNICA Bucharest, 1-7 Gh. Polizu St., District 1, 011061 Bucharest, Romania
- National Institute of Materials Physics, Atomistilor 405A, 077125 Magurele, Romania
- Faculty of Physics, University of Bucharest, Atomistilor 405, 077125 Magurele, Romania
| | - Radu C. Racovita
- Department of Inorganic Chemistry, Physical Chemistry and Electrochemistry, Faculty of Chemical Engineering and Biotechnologies, National University of Science and Technology POLITEHNICA Bucharest, 1-7 Gh. Polizu St., District 1, 011061 Bucharest, Romania
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5
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Chung Y, Green WH. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. Chem Sci 2024; 15:2410-2424. [PMID: 38362410 PMCID: PMC10866337 DOI: 10.1039/d3sc05353a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024] Open
Abstract
Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol-1 for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.
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Affiliation(s)
- Yunsie Chung
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
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Liu Y, Pickard FC, Sluggett GW, Mustakis IG. Robust fragment-based method of calculating hydrogen atom transfer activation barrier in complex molecules. Phys Chem Chem Phys 2024; 26:1869-1880. [PMID: 38175161 DOI: 10.1039/d3cp05028a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Dynamic processes driven by non-covalent interactions (NCI), such as conformational exchange, molecular binding, and solvation, can strongly influence the rate constants of reactions with low activation barriers, especially at low temperatures. Examples of this may include hydrogen-atom-transfer (HAT) reactions involved in the oxidative stress of an active pharmaceutical ingredient (API). Here, we develop an automated workflow to generate HAT transition-state (TS) geometries for complex and flexible APIs and then systematically evaluate the influences of NCI on the free activation energies, based on the multi-conformational transition-state theory (MC-TST) within the framework of a multi-step reaction path. The two APIs studied: fesoterodine and imipramine, display considerable conformational complexity and have multiple ways of forming hydrogen bonds with the abstracting radical-a hydroxymethyl peroxyl radical. Our results underscore the significance of considering conformational exchange and multiple activation pathways in activation calculations. We also show that structural elements and NCIs outside the reaction site minimally influence TS core geometry and covalent activation barrier, although they more strongly affect reactant binding and consequently the overall activation barrier. We further propose a robust and economical fragment-based method to obtain overall activation barriers, by combining the covalent activation barrier calculated for a small molecular fragment with the binding free energy calculated for the whole molecule.
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Affiliation(s)
- Yizhou Liu
- Analytical Research and Development, Pfizer Research and Development, 445 Eastern Point Road, Groton, CT 06340, USA.
| | - Frank C Pickard
- Pharmaceutical Sciences, Pfizer Research & Development, Groton, CT 06340, USA
- Medicine Design, Pfizer Research & Development, Cambridge, MA 02139, USA
| | - Gregory W Sluggett
- Analytical Research and Development, Pfizer Research and Development, 445 Eastern Point Road, Groton, CT 06340, USA.
| | - Iasson G Mustakis
- Chemical Research & Development, Pfizer Research & Development, Groton, CT 06340, USA
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Deem MC, Cai I, Derasp JS, Prieto PL, Sato Y, Liu J, Kukor AJ, Hein JE. Best Practices for the Collection of Robust Time Course Reaction Profiles for Kinetic Studies. ACS Catal 2023. [DOI: 10.1021/acscatal.2c05045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Madeleine C. Deem
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Isabelle Cai
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Joshua S. Derasp
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Paloma L. Prieto
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Yusuke Sato
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Junliang Liu
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Andrew J. Kukor
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Jason E. Hein
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
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Johnson MS, Dong X, Grinberg Dana A, Chung Y, Farina D, Gillis RJ, Liu M, Yee NW, Blondal K, Mazeau E, Grambow CA, Payne AM, Spiekermann KA, Pang HW, Goldsmith CF, West RH, Green WH. RMG Database for Chemical Property Prediction. J Chem Inf Model 2022; 62:4906-4915. [PMID: 36222558 DOI: 10.1021/acs.jcim.2c00965] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.
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Affiliation(s)
- Matthew S Johnson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Alon Grinberg Dana
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.,The Wolfson Department of Chemical Engineering, Grand Technion Energy Program (GTEP), Technion─Israel Institute of Technology, Haifa3200003, Israel
| | - Yunsie Chung
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - David Farina
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts02115, United States
| | - Ryan J Gillis
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Mengjie Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Nathan W Yee
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Katrin Blondal
- School of Engineering, Brown University, Providence, Rhode Island02912, United States
| | - Emily Mazeau
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts02115, United States
| | - Colin A Grambow
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - A Mark Payne
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Hao-Wei Pang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - C Franklin Goldsmith
- School of Engineering, Brown University, Providence, Rhode Island02912, United States
| | - Richard H West
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts02115, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
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Spiekermann KA, Pattanaik L, Green WH. Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy. J Phys Chem A 2022; 126:3976-3986. [PMID: 35727075 DOI: 10.1021/acs.jpca.2c02614] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP. Our model uses 75% fewer parameters than previous studies, an improved reaction representation, and proper data splits to accurately estimate performance on unseen reactions. Using information from only the reactant and product, our model quickly predicts barrier heights with a testing MAE of 2.6 kcal mol-1 relative to the coupled-cluster data, making it more accurate than a good density functional theory calculation. Furthermore, our results show that future modeling efforts to estimate reaction properties would significantly benefit from fine-tuning calibration using a transfer learning technique. We anticipate this model will accelerate and improve kinetic predictions for small molecule chemistry.
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Affiliation(s)
- Kevin A Spiekermann
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Lagnajit Pattanaik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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