Plausibility checks for generated molecule poses.
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Updated
Jun 3, 2024 - Python
Plausibility checks for generated molecule poses.
[ICLR 2024] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
Pose checks for 3D Structure-based Drug Design methods
Code for AAAI24 paper Text-Guided Molecule Generation with Diffusion Language Model
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
[ICLR 2024] Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
OM-Diff: Inverse-design of organometallic catalysts with guided equivariant denoising diffusion
A reinforcement learning library for material and molecule optimization
DrugAssist: A Large Language Model for Molecule Optimization
[NeurIPS'23] Source code of "Data-Centric Learning from Unlabeled Graphs with Diffusion Model": A data-centric transfer learning framework with diffusion model on graphs.
Code for the paper "Exploiting Pretrained Biochemical Language Models for Targeted Drug Design", to appear in Bioinformatics, Proceedings of ECCB2022.
exploring latent space of MoLeR AI for drug discovery
The implementation, training and evaluation of a Structure Seer machine learning model designed for reconstruction of adjacency of a molecular graph from the labelling of its nodes.
Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation
Official Code Repository for the paper "Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations" (ICML 2022)
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design
Comparisons of Drug Generation Models
Multiresolution Equivariant Graph Variational Autoencoder (MGVAE) https://arxiv.org/abs/2106.00967
Higher-order permutation-equivariant graph variational autoencoder to generate molecules in multiresolution manner. https://iopscience.iop.org/article/10.1088/2632-2153/acc0d8
Hierarchical generative and regressive machine learning for next generation materials screening
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