GitHub

Understanding-Molecule-Solubility-with-Graph-Neural-Networks

To Develop a Graph Neural Network (GNN) model for predicting the solubility of molecules using the ESOL dataset from the MoleculeNet benchmark. The GNN is trained on a set of molecules from the esol ...
GitHub

rnepal2/Solubility-Prediction-with-Graph-Neural-Networks

Predicting the aqueous solubility of small organic molecules is a critical step in early-stage drug discovery, influencing a compound's absorption, distribution, metabolism, and excretion (ADME) ...
Europe PMC

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. In this work, surrogated model-based methods were developed to accurately predict the solubility ...
Europe PMC

SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network.

Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling the rapid assessment of compound properties before costly and time-consuming laboratory experiments. With ...
C&EN

Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The solubility of chemical substances in water is a critical parameter in ...
C&EN

ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks

Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co., Ltd., Shanghai 200131, China ...