Rich data and powerful machine learning models allow us to design drugs for a specific
protein target\textit {in silico}. Recently, the inclusion of 3D structures during targeted drug …

Molecule generation, especially generating 3D molecular geometries from scratch (ie, 3D de
novo generation), has become a fundamental task in drug design. Existing diffusion based …

Deep generative models have recently achieved superior performance in 3D molecule
generation. Most of them first generate atoms and then add chemical bonds based on the …

Quantum chemical (QC) property prediction is crucial for computational materials and drug
design, but relies on expensive electronic structure calculations like density functional theory …

With the development of computer-assisted techniques, research communities, including
biochemistry and deep learning, have been devoted into the drug discovery field for over a …

Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
Existing structured-based drug design methods treat all ligand atoms equally, which ignores …

Data-driven predictive methods that can efficiently and accurately transform protein
sequences into biologically active structures are highly valuable for scientific research and …

Recent developments in deep learning have made remarkable progress in speeding up the
prediction of quantum chemical (QC) properties by removing the need for expensive …

Predicting energetically favorable 3-dimensional conformations of organic molecules
frommolecular graph plays a fundamental role in computer-aided drug discovery research …

Designing drugs capable of binding to the structure of target proteins for treating diseases is
essential in drug development. Recent remarkable advancements in geometric deep …