ArticleFrontiers in genetics2019
ContraDRG: Automatic Partial Charge Prediction by Machine Learning.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Polarizable Water Model with Ab Initio Neural Network Dynamic Charges and Spontaneous Charge Transfer.Journal of chemical theory and computation · 2025Article
- The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review.Mini reviews in medicinal chemistry · 2024Review
- DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.Journal of chemical information and modeling · 2023Article
- Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges.Journal of chemical information and modeling · 2023Article
- Extending machine learning beyond interatomic potentials for predicting molecular properties.Nature reviews. Chemistry · 2022Review
- Recent progress in general force fields of small molecules.Current opinion in structural biology · 2022Review
- Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
In recent years, machine learning techniques have been widely used in biomedical research to predict unseen data based on models trained on experimentally derived data. In the current study, we used machine learning algorithms to emulate computationally complex predictions in a reverse engineering-like manner and developed ContraDRG, a software that can be used to predict partial charges for small molecules based on PRODRG and Automated Topology Builder (ATB) predictions. Both tools generate molecular topology files, including the partial atomic charge, by using different procedures. We show that ContraDRG can accurately predict partial charges in a fraction of the time, because it exploits existing complex models with intensive calculations by using machine learning techniques and thus can also be applied for screening projects with large amounts of molecules. We provide ContraDRG as a web server, which can be used to automatically assign partial charges to incoming user-specified molecules by using our machine learning models. In this study, we compared ContraDRG with PRODRG and ATB in regard of predictivity by statistical methods. ContraDRG allows predicting ATB-derived partial charges with an
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Registered trials
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