Deep reinforcement learning for de novo drug design
Sign Up to like & getrecommendations! Published in 2018 at "Science Advances"
DOI: 10.1126/sciadv.aap7885
Abstract: We introduce an artificial intelligence approach to de novo design of molecules with desired physical or biological properties. We have devised and implemented a novel computational strategy for de novo design of molecules with desired… read more here.
Keywords: models trained; design; reinforcement learning; deep reinforcement ... See more keywords
SMILES-based deep generative scaffold decorator for de-novo drug design
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Cheminformatics"
DOI: 10.1186/s13321-020-00441-8
Abstract: Molecular generative models trained with small sets of molecules represented as SMILES strings can generate large regions of the chemical space. Unfortunately, due to the sequential nature of SMILES strings, these models are not able… read more here.
Keywords: models trained; smiles based; scaffold; chemistry ... See more keywords
Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Cheminformatics"
DOI: 10.1186/s13321-025-01041-0
Abstract: This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained using ligand-based representations. We propose a structured approach to data feature selection, taking a… read more here.
Keywords: ligand based; models trained; admet predictions; model ... See more keywords
Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
Sign Up to like & getrecommendations! Published in 2022 at "JCO Clinical Cancer Informatics"
DOI: 10.1200/cci.21.00170
Abstract: PURPOSE Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to… read more here.
Keywords: robustness models; oncology; learning models; deep learning ... See more keywords