A5036964563- Computational Drug Discovery Methods
- Chemistry and Chemical Engineering
- Free Radicals and Antioxidants
- Machine Learning in Materials Science
- Organic Chemistry Cycloaddition Reactions
- Climate change and permafrost
- Advanced Nanomaterials in Catalysis
- Catalytic C–H Functionalization Methods
- Material Properties and Failure Mechanisms
- Plant Molecular Biology Research
- Asphalt Pavement Performance Evaluation
- Nanoplatforms for cancer theranostics
- Synthesis and Catalytic Reactions
- Agriculture, Soil, Plant Science
- Plant Reproductive Biology
- Soil Carbon and Nitrogen Dynamics
- Synthesis and Biological Evaluation
- Groundwater and Isotope Geochemistry
- Polymer Synthesis and Characterization
- Chemical Reaction Mechanisms
- Metabolomics and Mass Spectrometry Studies
- Soil and Unsaturated Flow
- Carbon and Quantum Dots Applications
- Plant Gene Expression Analysis
- Chemical Synthesis and Reactions
Henan University
2025
Tsinghua University
2013-2024
Institute of Soil and Water Conservation
2024
Northwest A&F University
2024
Tianjin Chengjian University
2024
Shihezi University
2021
While many approaches to predict aqueous pKa values exist, the fast and accurate prediction of non-aqueous is still challenging. Based on iBonD experimental database (39 solvents), a holistic model was established using machine learning. Structural physical-organic-parameter-based descriptors (SPOC) were introduced represent electronic structural features molecules. The models trained with neural network or XGBoost algorithm showed best performance low MAE value 0.87 units. approach allows...
Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) (E), which proved to be useful tool rationalization chemical reactivity. this study, holistic prediction model was developed through machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical solvent features, purpose. With 1115 nucleophiles, 285 electrophiles, 22 solvents,...
Abstract While many approaches to predict aqueous p K a values exist, the fast and accurate prediction of non‐aqueous is still challenging. Based on iBonD experimental database (39 solvents), holistic model was established using machine learning. Structural physical‐organic‐parameter‐based descriptors (SPOC) were introduced represent electronic structural features molecules. The models trained with neural network or XGBoost algorithm showed best performance low MAE value 0.87 units. approach...
Comprehensive Summary Bond dissociation energy (BDE), which refers to the enthalpy change for homolysis of a specific covalent bond, is one basic thermodynamic properties molecules. It very important understanding chemical reactivities, and transformations. Here, machine learning‐based comprehensive BDE prediction model was established based on i BonD experimental dataset calculated by St. John et al . D ifferential S tructural P hysic OC hemical (D‐SPOC) descriptors that reflected changes...
Abstract Tumor recurrence and wound infection present significant challenges to patient recovery following surgery, underscoring the need for effective therapeutic strategies improve prognosis by mitigating these complications. This study introduces novel synthesis methods honeycomb‐like CuMnOx nanozymes CuO 2 nanoflowers with multienzymatic activity pH‐responsive properties. The enzymatic activities of can be regulated pH changes in tumor or microenvironments, while overcome limitation...
Abstract Background MicroRNAs (miRNAs) and other types of small regulatory RNAs play critical roles in the regulation gene expression at post-transcriptional level plants. Cotton is one most economically important crops, but little known about miRNAs during cotton fiber elongation. Results Here, we combined high-throughput sequencing with computational analysis to identify (sRNAs) related elongation Gossypium hirsutum L. ( G. ). The sequence confirmed 79 miRNA families elongating cells...
Feature representations, or descriptors, are machines' chemical language that largely shapes the prediction capability, generalizability and interpretability of machine learning models. To develop a generally applicable descriptor is highly warranted for chemists to deal with conventional tasks in context sparsely distributed small datasets. Inspired by chemist's vision on molecules, we presented herein an ensemble descriptor, SPOC, curated principles physical organic chemistry integrates...
近年来,由于计算能力、大数据和算法的不断进步,人工智能(Artificial intelligence,AI)重新兴起,已成为诸多研究领域变革性发展背后的重要推动力.机器学习(Machine learning,ML)是人工智能一个重要的研究领域.随着化学信息学的发展,机器学习在化学领域展现出巨大的发展潜力,也为有机化学的发展带来了新的机遇.为帮助有机化学家了解这一新兴领域,对如何将机器学习策略应用于有机化学研究做简单介绍,同时,概括总结了机器学习在化合物性质预测、分子从头设计、化学反应预测、逆合成分析和智能合成机器方面的应用实例,分析讨论了当前机器学习在有机化学领域面临的挑战和难题.
In this study, 3-benzoylisoxazolines were synthesized by reacting alkenes with various α-nitroketones using chloramine-T as the base. The scope of and is extensive, including different alkynes to form isoxazolines isoxazoles. use chloramine-T, low-cost, easily handled, moderate base for 1,3-dipolar cycloaddition attractive.
To enhance the compatibility of high-content desulfurized-rubber-modified asphalt (DRMA), innovative selection cerium hard acid is employed in this study to investigate its impact on DRMA. The results indicate that when optimal content stearate 1%, modified improved. On microscopic level, it observed total heat absorption decreases and thermal storage stability enhanced as measured by differential scanning calorimetry. macroscopic gives smallest difference softening point, stability, best...
The acid dissociation constant p K a dictates molecule’s ionic status, and is critical physicochemical property in rationalizing acid-base chemistry solution many biological contexts. Although numerous theoretic approaches have been developed for predicating aqueous , fast accurate prediction of non-aqueous s has remained major challenge. On the basis i BonD experimental database curated across 39 solvents, holistic model was established by using machine learning approach. Structural...
Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) (E), which proved to be useful tools rationalization chemical reactivity. this study, holistic prediction model was developed through machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical, solvent features, purpose. With 1115 nucleophiles, 285 electrophiles 22 solvents,...