Article
Biochemistry & Molecular Biology
Xixi Li, Hao Yang, Yuanyuan Zhao, Qikun Pu, Tingzhi Xu, Rui Li, Yu Li
Summary: This study developed a method to verify the synthesizability of previously designed synthetic musk (SM) derivatives, aiming to screen for synthesizable, high-performance, and environmentally friendly SM derivatives. Three SM derivatives (D52, D37, and D25) were screened and recommended due to their good performances, including high synthesizability, odor sensitivity, low abortion risk, and bioaccumulation ability. The study also analyzed the synthesizability mechanism of SM derivatives and identified key factors for improved synthesis. This research broke the synthesizability bottleneck of theoretical SM derivatives design and advanced the mechanism of screening functional derivatives.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Ridha Ben Said, Riadh Hanachi, Seyfeddine Rahali, Mohammed A. M. Alkhalifah, Faisal Alresheedi, Bahoueddine Tangour, Majdi Hochlaf
Summary: Researchers performed density functional theory calculations and quantitative structure-activity relationship evaluations on pyrazole derivatives for epidermal growth factor receptor inhibiting activity, proposing a virtual screening protocol. The linear regression model showed a strong correlation between predicted and observed values, allowing for efficient preclinical phase optimization.
JOURNAL OF COMPUTATIONAL CHEMISTRY
(2021)
Article
Biochemistry & Molecular Biology
Ignacio Aliagas, Alberto Gobbi, Man-Ling Lee, Benjamin D. Sellers
Summary: In drug discovery, the lipophilicity of molecules, assessed by partition and distribution coefficients (logP and logD), significantly affects the bioactivity and bioavailability of potential drugs. This study proposes an integrated machine learning QSAR modeling approach to predict logD, using experimental data, ClogP, and pKa as model descriptors. The results demonstrate that this approach improves the prediction of lipophilicity and extends the applicability of logD and logP predictions compared to commercial software.
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
(2022)
Article
Physics, Multidisciplinary
Annalisa Paolone, Simone Di Muzio, Oriele Palumbo, Sergio Brutti
Summary: This study proposes a reliable algorithm to estimate the electrochemical stability of ionic liquids by evaluating the linear dependence between the anodic limit and HOMO level of 27 different anions. The solvation energy of the ions is found to have a significant impact on the anodic limit, and an empirical model considering this effect is proposed for the first time. This research is of great importance for the rapid discovery of suitable anions capable of sustaining high potentials.
Article
Engineering, Environmental
Ralf-Uwe Ebert, Ralph Kuehne, Gerrit Schueuermann
Summary: The Koa value is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. A newly developed fragment model outperforms other prediction methods in terms of accuracy, and is implemented in the ChemProp software for public use. Additionally, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental and predicted Koa values.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Engineering, Environmental
Tengyi Zhu, Cuicui Tao
Summary: Thirteen QSPR models were developed in this study, with the GBDT model showing remarkable superiority in fitting, robustness and predictability. The main internal factors affecting the partition process were revealed to be molecular size, branches and types of bonds. Unlike existing models based on single category compounds, the models developed in this study considered multiple classes compounds, making their application domain more comprehensive.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Biochemical Research Methods
Zhenxing Wu, Minfeng Zhu, Yu Kang, Elaine Lai-Han Leung, Tailong Lei, Chao Shen, Dejun Jiang, Zhe Wang, Dongsheng Cao, Tingjun Hou
Summary: A study on learning QSAR models using various ML algorithms for 14 public datasets showed that rbf-SVM, rbf-GPR, XGBoost, and DNN generally perform better than other algorithms. SVM and XGBoost are recommended for regression learning on small datasets, while XGBoost is an excellent choice for large datasets. Ensemble models integrating multiple algorithms can improve prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
(2021)
Review
Biochemistry & Molecular Biology
Arif Mermer
Summary: Machine learning methods have attracted increasing interest in chemistry for the design of targeted bioactive compounds, and there are various online platforms available for their application. This paper examines literature data on the prediction of activity for heterocyclic compounds, biological activity results, pharmacophore-based studies, synthesis, and finding possible inhibitors using different machine learning methods.
MOLECULAR DIVERSITY
(2022)
Review
Engineering, Biomedical
Jacob Kerner, Alan Dogan, Horst von Recum
Summary: Machine learning has been widely utilized in various fields, including biomaterials, optimizing data collection and analysis. Recent advances in biomaterials have focused on quantitative structure properties relationships, introducing four basic models for rapid development and addressing the lack of machine learning implementation in the field. This article aims to spark greater interest and awareness in utilizing computational methods for biomaterials research.
ACTA BIOMATERIALIA
(2021)
Article
Chemistry, Physical
Zuo-Yuan Zhang, Xiaohui Wang, Qiaole He, Zhaoxi Sun
Summary: This study develops low-cost machine-learning estimators to further explore the chemical accuracy frontier. By collecting experimental data, a dataset is created to accurately predict solvation and water-ILs biphasic partition.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Article
Engineering, Environmental
Ralf-Uwe Ebert, Ralph Kuehne, Gerrit Schueuermann
Summary: Henry's law constant is crucial for evaluating the fate of organic compounds in the environment, such as accumulation, contamination, and airborne impact. A new fragment model has been developed to predict the air/water partition coefficient (log Kaw) based on molecular structures. This model outperforms other existing models in terms of accuracy and robustness, and is available to the public through ChemProp software. It can also be combined with other models to predict log Kaw at different temperatures.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Jiri Czernek, Jiri Brus, Vladimira Czernekova
Summary: There is a growing interest in quantitatively predicting the intermolecular binding energy of large complexes. The domain-based local pair natural orbital (DLPNO) scheme is an important quantum chemical technique that can be used for such predictions. In this study, a DLPNO-based focal-point method is developed to obtain values that are close to the canonical CCSD(T)/CBS counterparts, allowing for routine checking of less expensive computational methods.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Energy & Fuels
Yi Wang, Luzian Lebovitz, Kedi Zheng, Yao Zhou
Summary: A novel weighted consensus clustering-based approach is proposed for bi-objective power network partition, allowing for Pareto improvement. Case studies on the IEEE 300-bus test system demonstrate the effectiveness and superiority of the proposed method.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Leila Saranjam, Elisabet Fuguet, Miroslava Nedyalkova, Vasil Simeonov, Francesc Mas, Sergio Madurga
Summary: A computational methodology based on Density-Functional Theory (DFT) was developed to determine the partition coefficient of a compound in SDS micelles solution. The most appropriate solvent combination for estimating the partition coefficient of SDS micelles was found to be propan-1-ol/water. This approach allows for estimating the partition coefficient orders of magnitude faster than classical molecular dynamics simulations.
Article
Green & Sustainable Science & Technology
T. Zhang, C. Wu, Z. Xing, J. Zhang, S. Wang, X. Feng, J. Zhu, X. Lu, L. Mu
Summary: In this study, machine learning combined with density functional theory (DFT) was used to analyze lignin structure and provide insights for the design of photocatalytic C-C cleavage systems. The random forest (RF) model showed the highest test accuracy and it was found that reaction conditions and oxygen played important roles in the photocatalytic degradation of lignin.
MATERIALS TODAY SUSTAINABILITY
(2022)
Article
Chemistry, Physical
Christopher O. Obondi, Gary N. Lim, Youngwoo Jang, Prajay Patel, Angela K. Wilson, Prashanth K. Poddutoori, Francis D'Souza
JOURNAL OF PHYSICAL CHEMISTRY C
(2018)
Article
Biochemistry & Molecular Biology
Yigitcan Eken, Prajay Patel, Thomas Diaz, Michael R. Jones, Angela K. Wilson
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
(2018)
Article
Chemistry, Multidisciplinary
Prajay Patel, Jiaqi Wang, Angela K. Wilson
JOURNAL OF COMPUTATIONAL CHEMISTRY
(2020)
Article
Chemistry, Multidisciplinary
Prajay Patel, Angela K. Wilson
JOURNAL OF COMPUTATIONAL CHEMISTRY
(2020)
Article
Chemistry, Applied
Prajay Patel, Angela K. Wilson
Article
Chemistry, Inorganic & Nuclear
Alon Chapovetsky, Prajay Patel, Cong Liu, Alfred P. Sattelberger, David M. Kaphan, Massimiliano Delferro
Article
Chemistry, Physical
Robert H. Wells, Suming An, Prajay Patel, Cong Liu, Rex T. Skodje
Summary: A theoretical approach for the study of supported atom catalysis is developed based on recent advances in the study of single-molecule kinetics. The disorder induced by the amorphous support material is divided into short-range and long-range components modeling covalent structures near the reaction center and bulk randomness respectively. The kinetics of hydrogenation reactions are analyzed using a probability distribution function, with results indicating the contribution of multiple catalytic pathways acting in concert.
JOURNAL OF PHYSICAL CHEMISTRY C
(2021)
Article
Chemistry, Physical
Prajay Patel, Robert H. Wells, David M. Kaphan, Massimiliano Delferro, Rex T. Skodje, Cong Liu
Summary: This study investigates the impact of surface heterogeneity on catalytic activity of supported organovanadium(III) catalysts, utilizing density functional theory (DFT) and kinetic Monte Carlo (kMC) simulations to model potential active sites and reveal reaction pathways, highlighting the importance of modeling surface heterogeneity in computational catalysis.
Article
Chemistry, Multidisciplinary
Mehrafshan G. Jafari, Dominik Fehn, Anders Reinholdt, Cristina Hernandez-Prieto, Prajay Patel, Michael R. Gau, Patrick J. Carroll, J. Krzystek, Cong Liu, Andrew Ozarowski, Joshua Telser, Massimiliano Delferro, Karsten Meyer, Daniel J. Mindiola
Summary: The synthesis of nitrogen complexes between V-V and V-III was studied, and it was found that these complexes exhibited different magnetic properties and stabilities.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2022)
Article
Chemistry, Physical
Alon Chapovetsky, Robert M. Kennedy, Ryan Witzke, Evan C. Wegener, Fulya Dogan, Prajay Patel, Magali Ferrandon, Jens Niklas, Oleg G. Poluektov, Ning Rui, Sanjaya D. Senanayake, Jose A. Rodriguez, Nestor J. Zaluzec, Lei Yu, Jianguo Wen, Christopher Johnson, Cynthia J. Jenks, A. Jeremy Kropf, Cong Liu, Massimiliano Delferro, David M. Kaphan
Summary: This article describes the application of Li-ion battery cathode and anode materials as redox non-innocent catalyst supports for the electronic tuning of a catalyst's active site. The activity of the catalyst for olefin hydrogenation was found to increase as a function of support reductive lithiation. Simulation results reveal the significant impact of surface redox states on the viability of the homolytic oxidative addition mechanism.
Article
Chemistry, Physical
Suming An, Prajay Patel, Cong Liu, Rex T. Skodje
Summary: This article analyzes catalysis from single active sites using stochastic Markov-state description. By eigenvalue decomposition and sensitivity analysis, the energies of controlling barriers and wells along the reaction routes can be related to the necessary eigenvalues and eigenvectors. The method is demonstrated for model problems and a physically realistic mechanism for an alkene hydrogenation reaction on a single-atom catalyst. Spectral analysis allows identification of a hierarchy of timescales from the single-molecule signal.
JOURNAL OF PHYSICAL CHEMISTRY A
(2022)
Article
Chemistry, Physical
Prajay Patel, Zheng Lu, Mehrafshan G. Jafari, Cristina Hernandez-Prieto, Pavel Zatsepin, Daniel J. Mindiola, David M. Kaphan, Massimiliano Delferro, Jeremy Kropf, Cong Liu
Summary: X-ray absorption near-edge structure (XANES) spectroscopy is a powerful tool for studying the structure and electronic properties of catalytic sites. This study presents a computational and experimental strategy to determine the bonding interactions in the XANES pre-edge region for organovanadium complexes. By predicting the energy and molecular structure of vanadium complexes, the characterization of a supported organovanadium olefin hydrogenation catalyst was successfully conducted.
JOURNAL OF PHYSICAL CHEMISTRY C
(2022)
Article
Chemistry, Physical
Suming An, Prajay Patel, Cong Liu, Rex T. Skodje
Summary: This article presents a theory-based optimization strategy using density functional theory to determine the transition states and intermediates of a low-dimensional coordinate representation for the heterogeneous active sites. It is applied to a vanadium catalyst on an amorphous SiO2 support and provides significant simplification in treating disordered systems through the use of a nonlinear optimization algorithm and energetic span theory.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)