Application of machine learning and deep learning methods for hydrated electron rate constant prediction

Zheng, Shourong; Guo, Wanqian; Li, Chao; Sun, Youxian; Zhao, Qi; Lü, Hao; Si, Qishi; Wang, Huazhe

doi:10.1016/j.envres.2023.115996

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Using the LGBM model, feature importance of the input descriptors was performed using Shapley additive explanations (SHAP) to examine the relative contribution of each descriptor to the predicted result (Figure S7). , This examination revealed two basic findings: First, the experimental descriptors (CNT type, CNT quantity, and ratio of dispersant to CNT) as a whole contributed more significantly to the model predictions. Second, molecular descriptors which captured the overall features of the molecular species (e.g., BCUT2D, Chi, SMR_VSA, and AvgIpc) contributed more than those which described more specific aspects of the molecule, such as the number of atoms (Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

Jintoku,

Futaba

2024

ACS Appl. Mater. Interfaces

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Dispersion represents a central processing method in the organization of nanomaterials; however, the strong interparticle interaction represents a significant obstacle to fabricating homogeneous and stable dispersions. While dispersants can greatly assist in overcoming this obstacle, the appropriate type is dependent on such factors as nanomaterial, solvent, experimental conditions, etc., and there is no general guide to assist in the selection from the vast number of possibilities. We report a strategy and successful demonstration of the machine-learning-based "Dispersant Explorer", which surveys and identifies suitable dispersants from open databases. Through the combined use of experimental and molecular descriptors derived from SMILES databases, the model showed exceptional predictive accuracy in surveying about ∼1000 chemical compounds and identifying those that could be applied as dispersants. Furthermore, fabrication of transparent conducting films using the predicted and previously unknown dispersant exhibited the highest sheet resistance and transmittance compared with those of other reported undoped films. This result highlights that, in addition to opening new avenues for novel dispersant discovery, machine learning has a potential to elucidate the chemical structures essential for optimal dispersion performance to assist in the advancement of the complex topic of nanomaterial processing.

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Section: Resultsmentioning

confidence: 99%

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

Jintoku,

Futaba

2024

ACS Appl. Mater. Interfaces

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“…The SHAP value of a feature is determined by the average of the feature’s contribution across all possible feature alignments in the feature set [ 38 ]. It measures the degree and direction of the descriptor’s contribution to the prediction result: higher absolute SHAP values indicate a higher contribution, and whether a SHAP value demonstrates positivity or negativity corresponds to the positive and negative impact of the descriptor on the prediction result [ 83 , 84 ]. The global importance of a feature is reflected by averaging the absolute SHAP values corresponding to all the samples in that feature [ 85 ].…”

Section: Methodsmentioning

confidence: 99%

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Tao,

Zhu

2024

Molecules

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Accurately predicting plant cuticle–air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

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“…We compared the results with those of traditional molecular similarity methods called fingerprinting. Here, we use Morgan or Circular Fingerprints which apply the Morgan algorithm to a set of atom invariants 56 , 57 . We will also verify these results using newly created natural product compound libraries that are not currently included in any database, such as the Compound Library of the Natural Products Research Laboratories (NPRL) of China Medical University Hospital (CMUH) in Taiwan 58 .…”

Section: Introductionmentioning

confidence: 99%

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Huang,

Yang,

Liao

et al. 2024

Sci Rep

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Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

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Application of machine learning and deep learning methods for hydrated electron rate constant prediction

Cited by 9 publications

References 34 publications

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

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