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. 2023 Nov 29;11:1292027. doi: 10.3389/fchem.2023.1292027

TABLE 4.

Examples of key AI-driven algorithms and methods for prediction of molecular properties.

Class Method description References/Examples
Dynamic Selection Techniques for dynamic selection of classifiers based on individual sample Cruz et al. (2018)
Ensemble learning Combination of multiple learners for performance improvement Svetnik et al. (2003); Sheridan et al. (2016); Kwon et al. (2019); Davronov and Adilova, (2021)
Fully Connected DL Fully connected deep learning network for Single-task QSAR analysis Ma et al. (2015)
GCN Multitask graph convolutional networks Montanari et al. (2019)
Fully Connected DL Fully connected deep learning network for Multi-task QSAR analysis Kearnes et al. (2016)
GCN PotentialNet family of graph convolutions for protein-ligand binding affinity Feinberg et al. (2018); Feinberg et al. (2020)
GNN Molecular Contrastive Learning Wang et al. (2021c)
MPNN Message passing neural networks for molecular property prediction (Yang X. et al. (2019); Stokes et al. (2020); Chen et al. (2021); Heid et al. (2023)
Graph Transformer Molecular encoding using hybrid MPNN-Transformer architectures Rong et al. (2021)
NLP inspired Autoencoder-based Molecular encoding and QSAR Winter et al. (2019)
NLP inspired Transformer based encoder model Honda et al. (2019); Payne et al. (2020); Irwin, Dimitriadis et al. (2022)
NLP inspired Transfer learning for NLP based classification tasks Li and Fourches (2020)
NLP inspired Siamese RNNs for QSAR Prediction Fernández-Llaneza et al. (2021)
Active Learning Retrosynthetic and combinatorial synthesis coupled with Active Learning Konze et al. (2019)
Explainable Artificial Intelligence Methods to provide interpretability to ML/DL models. These include approaches for explaining their predictions, quantifying their uncertainty, and estimating their applicability domains Interpretability Ribeiro et al. (2016); Lundberg and Lee, (2017); Nori et al. (2019); Rodríguez-Pérez and Bajorath, (2021)
Uncertainty estimation Liu et al. (2018a); Cortés-Ciriano and Bender, (2019); Gawlikowski et al. (2021); Zhong et al. (2022)
Applicability domain Liu and Wallqvist, (2019), R. P. Sheridan, (2015); Schroeter et al. (2007); Supratik et al. (2018)