Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure–Potency Fingerprint

Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure–activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed mo...

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Vydané v:	Biomolecules (Basel, Switzerland) Ročník 13; číslo 2; s. 393
Hlavní autori:	Janela, Tiago, Takeuchi, Kosuke, Bajorath, Jürgen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 18.02.2023 MDPI
Predmet:	Algorithms Bioactive compounds Chemical properties Chemistry conditional variational autoencoder Decision trees Deep learning Drugs fingerprints Health aspects Ligands Machine Learning Neural networks Neural Networks, Computer Optimization Organic compounds Pharmaceutical research Pharmaceutical sciences potency prediction Quantitative Structure-Activity Relationship Structure Structure-activity relationship (Pharmacology) Structure-activity relationships Germany conditional variational autoencoder bioactive compounds potency prediction machine learning fingerprints
ISSN:	2218-273X, 2218-273X
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Shrnutí:	Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure–activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed molecular fingerprint (FP) representation. This structure–potency fingerprint (SPFP) combines different modules accounting for the structural features of active compounds and their potency values in a single bit string, hence unifying structure and potency representation. This encoding enables the derivation of a conditional variational autoencoder (CVAE) using SPFPs of training compounds and apply the model to predict the SPFP potency module of test compounds using only their structure module as input. The SPFP–CVAE approach correctly predicts the potency values of compounds belonging to different activity classes with an accuracy comparable to support vector regression (SVR), representing the state-of-the-art in the field. In addition, highly potent compounds are predicted with very similar accuracy as SVR and deep neural networks.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2218-273X 2218-273X
DOI:	10.3390/biom13020393