Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit

Duarte Ramos Matos, Guilherme; Pak, Steven; Rizzo, Robert C.

doi:10.1021/acs.jcim.3c01031

Cited by 6 publications

(4 citation statements)

References 57 publications

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“…were generated using the JRgui graphical user interface (GUI) [25,28] powered by the Tkinter package. This tool computes descriptors integrated into RDKit [26] and generates grid representations of 3D molecular structures [29]. Leveraging the JRgui and RDKit toolkit [25,28,29], we extracted a comprehensive set of approximately 200 descriptors.…”

Section: Molecular Descriptors or Features Represen�ng Measurable Dat...mentioning

confidence: 99%

“…This tool computes descriptors integrated into RDKit [26] and generates grid representations of 3D molecular structures [29]. Leveraging the JRgui and RDKit toolkit [25,28,29], we extracted a comprehensive set of approximately 200 descriptors. However, a�er a thorough examina�on, only 6 descriptors for NHOC and 40 for density (ρ) were me�culously selected from this set to train the models.…”

Section: Molecular Descriptors or Features Represen�ng Measurable Dat...mentioning

confidence: 99%

“…1 of the present study begins with data collection from multiple databases and resources. Descriptor generation follows, utilizing tools like Rdkit and Jugui [25,28,29]. Subsequently, a supervised training model is chosen, employing an appropriate algorithm such as SVM.…”

Section: Machine Learning Model Trainingmentioning

confidence: 99%

“…Note that gravimetric NHOC (NHOCG) and density (ρ) are obtained from ML, and other proper�es/descriptors such as volumetric NHOC (NHOCV), H/C ra�o, and the total number of rings (Nring) are derived. The total number of rings (Nring) in Table 3 can be obtained from RDKit [28,29] or counted manually from the structures. As seen in the table, almost all these compounds contain either triangular rings or rectangular rings with acute angles or pentagon rings, indica�ng that they are strained with possibly higher internal energy.…”

Section: Performance Of the Svm Trained Model On Nhocmentioning

confidence: 99%

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Sustainable aviation fuels (SAFs) are crucial for addressing carbon emissions in the aviation industry. With a focus on SAFs, the research aims to establish a quantitative structure-property relationship for polycyclic hydrocarbons (PCHCs) and their net heat of combustion (NHOC) using the innovative approach of machine learning (ML). The model trained with support vector machine (SVM) algorithms in ML is selected as it demonstrates superior performance over other available algorithms with a high coefficient of determination (R2) and low mean absolute error (MAE) of 27.821 KJ/mol for 20% test data. Using the optimum SVM model, thirty-five potential PCHCs are identified as SAF candidates from C6 to C15 sourced from reputable scientific literature and databases. Furthermore, structural analysis revealed that high-performance PCHCs typically consist of saturated alkanes with multiple 3, 4, and 5-membered rings, suggesting that strained energy plays a role in their high energy density. The model obtained from ML can be employed to screen new hydrocarbons for their suitability as SAF candidates before costly experiments and ASTM evaluations.

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Section: Molecular Descriptors or Features Represen�ng Measurable Dat...mentioning

confidence: 99%

Section: Molecular Descriptors or Features Represen�ng Measurable Dat...mentioning

confidence: 99%

Section: Machine Learning Model Trainingmentioning

confidence: 99%

Section: Performance Of the Svm Trained Model On Nhocmentioning

confidence: 99%

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A boiling point prediction method based on machine learning for potential insulating gases

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Zha,

Ling

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Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning

Dong,

Li,

Liu

2024

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An expansive array of graph-based models has been utilized for accurate prediction of the structure−property relation of polymers. However, these approaches notably underutilize unsupervised structural information. Concentrating on the domain of heterocyclic polymers, particularly polyimides, this study delves into the glass transition temperature (T g ) prediction, aiming to fully exploit the potential within both the global and local structures of molecules. To achieve this, a graph reinforcement learning framework termed Molecular Structural Regularized Graph Convolutional Network with Reinforcement Learning (MSRGCN-RL) is proposed. Experimental results highlight the crucial role of both global and local structural regularization in precise T g prediction. Concurrently, optimization of MSRGCN training through RL proves essential. This research leads the way in integrating Graph Neural Networks (GNNs) with reinforcement learning methodologies for the property prediction of polymers.

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Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit

Cited by 6 publications

References 57 publications

Machine Learning for High Energy Density Hydrocarbons for Sustainable Aviation Fuel

Machine Learning for High Energy Density Hydrocarbons for Sustainable Aviation Fuel

A boiling point prediction method based on machine learning for potential insulating gases

Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning

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