As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging.
Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology.
Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery as it guides the identification and optimization of molecules that effectively bind to target proteins.
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics.
Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property.
The high synthetic accessibility of the generated molecules is implicitly considered while preparing the fragment library with the BRICS decomposition method.
Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs.
All models reported so far are based on either variational autoencoder (VAE) or generative adversarial network (GAN).
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest.
Ranked #2 on Molecular Graph Generation on InterBioScreen
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design.
Deep neural networks have outperformed existing machine learning models in various molecular applications.
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions.
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design.
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery.