miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models...

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Bibliographic Details
Published in:Biomolecules (Basel, Switzerland) Switzerland), 2022-12, Vol.13 (1), p.29
Main Authors: Cai, Chenjing, Lin, Haoyu, Wang, Hongyi, Xu, Youjun, Ouyang, Qi, Lai, Luhua, Pei, Jianfeng
Format: Article
Language:English
Subjects:
Accuracy
active learning
Benchmarking
Clinical trials
Data collection
Datasets
Deep learning
Drug Development
Drug Discovery
Drugs
ensemble learning
graph neural network
Learning
Machine Learning
Neural networks
Neural Networks, Computer
Prediction models
subdivisional drug-likeness prediction
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Summary:The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.
ISSN:2218-273X
2218-273X
DOI:10.3390/biom13010029