Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry
Abstract
:1. Introduction
2. Artificial Intelligence, Machine or Deep Learning—Magic Tools or a Viral Buzz
3. From Chemical Compounds to Drugs and Materials: Defining the Problem
4. Self-Organizing Mapping of Molecular Representations
5. Deep Learning for Processing Molecular Data in Drug Design
Problem | Data/Learning Type | Reference |
---|---|---|
DNA subregion binding | In vitro HTS/convolutional neural networks | [47] |
Protein function | 3D electron density/convolutional filters | [48] |
Genomics | Gene expression contrastive divergence (unsupervised) [49]; back-propagation (supervised) [50]; multilayer perceptron [51] supervised | [49,50,51] |
Pharmacodynamics (DeepDTI) | Drug-protein interaction/unsupervised/then supervised [52]; supervised [53] | [52,53] |
DeepAffinity | Compound-protein affinity/supervised | [54] |
DeepTox toxicity | Toxic data/multi-task networks (supervised) | [55] |
Drug IC50 | Mol. descriptors/supervised | [56] |
VAE chemical properties | SMILES; molecular graphs/unsupervised | [45,57,58,59,60] |
VAE/GENTRL DDR1 small molecule design | SMILES; Kohonen-SOM based reward function/semi-supervised | [46] |
VAE/Graph encoders | Molecular graphs/unsupervised | [61,62,63] |
Protein-ligand pair | SMILES; voxels/unsupervised | [64,65] |
CMap/gen perturbagens | Gen-expression profiles/unsupervised | [66] |
Scaffold generation | molecular graphs; physicochemical properties; fragments/unsupervised | [67,68,69,70,71,72,73,74,75] |
6. Feature Engineering vs. Feature Learning—A Lesson from Deep Retrosynthetic Approaches
7. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Polanski, J. Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry. Int. J. Mol. Sci. 2022, 23, 2797. https://doi.org/10.3390/ijms23052797
Polanski J. Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry. International Journal of Molecular Sciences. 2022; 23(5):2797. https://doi.org/10.3390/ijms23052797
Chicago/Turabian StylePolanski, Jaroslaw. 2022. "Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry" International Journal of Molecular Sciences 23, no. 5: 2797. https://doi.org/10.3390/ijms23052797