Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning

Lyngdoh, Gideon A.; Li, Hewenxuan; Zaki, Mohd; Krishnan, N. M. Anoop; Das, Sumanta

doi:10.1038/s41598-020-78368-1

Cited by 15 publications

(6 citation statements)

References 50 publications

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“…Interpretability was particularly important because the best model they could obtain was an artificial neural network that yielded a probability vector. The importance of each input feature to the final prediction was determined using the SHAP approach [80].…”

Section: Ai For Insightful MD Data Analysismentioning

confidence: 99%

Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

Dhudum,

Ganeshpurkar,

Pawar

2024

DDC

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The drug discovery and development process is very lengthy, highly expensive, and extremely complex in nature. Considering the time and cost constraints associated with conventional drug discovery, new methods must be found to enhance the declining efficiency of traditional approaches. Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Advancements in AI and machine learning (ML) techniques have revolutionized their applications to drug discovery and development. This review illuminates the profound influence of AI on diverse aspects of drug discovery, encompassing drug-target identification, molecular properties, compound analysis, drug development, quality assurance, and drug toxicity assessment. ML algorithms play an important role in testing systems and can predict important aspects such as the pharmacokinetics and toxicity of drug candidates. This review not only strengthens the theoretical foundation and development of this technology, but also explores the myriad challenges and promising prospects of AI in drug discovery and development. The combination of AI and drug discovery offers a promising strategy to overcome the challenges and complexities of the pharmaceutical industry.

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Section: Ai For Insightful MD Data Analysismentioning

confidence: 99%

Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

Dhudum,

Ganeshpurkar,

Pawar

2024

DDC

View full text Add to dashboard Cite

show abstract

“…Across all the models used, the R² values were close to 1, while the MSE values showed slight variations. In order to obtain accurate and reliable predictions, the choice of model should be based on a combination of high R² and low MSE results [95]. Additionally, a model with extremely high values for the training set and lower values for the test set may be overfitted, and a good combination of results for both sets indicates a stronger model.…”

Section: Performance Of the Models With Different Data Imputation Met...mentioning

confidence: 99%

A Novel Interpretable Machine Learning Model Approach for the Prediction of TiO 2 Photocatalytic Degradation of Air Contaminants

Schossler,

Ojo,

Jiang

et al. 2023

Preprint

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Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). A data collection protocol was developed to collect data from published papers to analyze the effects of a variety of contributing factors on the photo-catalytic degradation performance. A large portion of data was found missing values. Imputation methods were used to estimate the missing values that allow the dataset to be fully utilized. Furthermore, a generative ML model Vanilla Gan model was utilized to create synthetic data to further augment the size of available dataset. The results indicated that data imputation allowed to fully utilize the limited amount of available dataset to achieve good ML prediction performance and prevent problems such as overfitting common with small-sized data. Besides, augmentation of experimental data with synthetic data significantly improved the ML prediction accuracy and reduced considerably the overfitting problems. The SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws. Interpretable ML models allows to shed light on the mechanism and present a promising tool in the prediction and assessment of the major contributing factors on the TiO2 photo-degradation rate of air contaminants. Besides direct findings on the potentials of ensembled ML models for TiO2 photocatalytic performance prediction, this study showed that imputation processes in data pre-processing to fill missing values and generative ML model for data augmentation allow to fully utilize the value of data, which is important for successful application of ML model for small and imperfect dataset commonly seen in engineering and science domains.

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“…Furthermore, machine learning is a useful approach when predicting molecular dynamical material behavior. Lyngdoh et al 146 integrated machine learning with MD to analyze the relationship of calcium‐silicate‐hydrate gel to create efficient cementitious binders. Since the best model they obtained was an artificial neural network, whose output consisted of a vector of probabilities, the interpretability was particularly necessary.…”

Section: Interpretability Of Deep Learning Modelsmentioning

confidence: 99%

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Bai

Liu

Tian

et al. 2021

WIREs Comput Mol Sci

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De novo drug design is a stationary way to build novel ligands in the confined pocket of receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation is a dynamical way to study the interaction mechanism between the ligands and receptors based on the molecular force field. De novo drug design and MD simulation are effective tools for novel drug discovery. With the development of technology, deep learning methods, and interpretable machine learning (IML) have emerged in the research area of drug design. Deep learning methods and IML can be used further to improve the efficiency and accuracy of de novo drug design and MD simulations. The application summary of deep learning methods for de novo drug design, MD simulations, and IML can further promote the technical development of drug discovery. In this article, two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective. The application progress of deep learning is also summarized for MD simulations. Furthermore, IML is introduced for the deep learning model interpretability of de novo drug design and MD simulations. Our paper deals with an interesting topic about deep learning applications of de novo drug design and MD simulations for the scientific community. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning

Cited by 15 publications

References 50 publications

Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

A Novel Interpretable Machine Learning Model Approach for the Prediction of TiO 2 Photocatalytic Degradation of Air Contaminants

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

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