MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications

This study introduces MolAI, a robust deep learning model designed for data-driven molecular descriptor generation. Utilizing a vast training data set of 221 million unique compounds, MolAI employs an autoencoder neural machine translation model to generate latent space representations of molecules....

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Vydáno v:Journal of chemical information and modeling Ročník 65; číslo 19; s. 9892
Hlavní autoři: Mahdizadeh, Sayyed Jalil, Eriksson, Leif A
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 13.10.2025
Témata:
Deep Learning
Drug Discovery - methods
Ligands
Neural Networks, Computer
Structure-Activity Relationship
ISSN:1549-960X, 1549-960X
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Shrnutí:This study introduces MolAI, a robust deep learning model designed for data-driven molecular descriptor generation. Utilizing a vast training data set of 221 million unique compounds, MolAI employs an autoencoder neural machine translation model to generate latent space representations of molecules. The model demonstrated exceptional performance through extensive validation, achieving an accuracy of >99.8% in regenerating input molecules from their corresponding latent space. This study showcases the effectiveness of MolAI-driven molecular descriptors by developing an ML-based model (iLP) that accurately predicts the predominant protonation state of molecules at neutral pH. These descriptors also significantly enhance ligand-based virtual screening and are successfully applied in a framework (iADMET) for predicting ADMET features with high accuracy. This capability of encoding and decoding molecules to and from latent space opens unique opportunities in drug discovery, structure-activity relationship analysis, hit optimization, molecular generation, and training infinite machine learning models.
Bibliografie:ObjectType-Article-1
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ISSN:1549-960X
1549-960X
DOI:10.1021/acs.jcim.5c00491