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Found 6 papers, 4 papers with code
Prediction of Protein Aggregation Propensity via Data-driven Approaches
Protein aggregation occurs when misfolded or unfolded proteins physically bind together, and can promote the development of various amyloid diseases.
AmorProt: Amino Acid Molecular Fingerprints Repurposing based Protein Fingerprint
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence.
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks
Predicting both accurate and reliable solubility values has long been a crucial but challenging task.
MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder
In this respect, this study proposes a molecular graph generative model based on the autoencoder for de novo design.
Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries
In this study, a platform consisting of a high-throughput screening and a machine-learning surrogate model for discovering superionic Li-SSEs among 20, 237 Li-containing materials is developed.
A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network
The prediction and evaluation of the biodegradability of molecules with computational methods are becoming increasingly important.
