Predicting Molecular Conformation via Dynamic Graph Score Matching

By Shitong Luo, Chence Shi, Minkai Xu, Jian Tang. 2021

120 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “, 2021) and fast generation of small molecule low-energy conformation ensembles (Ganea et al., 2021b; Luo et al., 2021; Xu et al., 2020; Shi et al., 2021).”
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (2022, methodology): “To overcome this problem, C ONF GF (Shi et al., 2021; Luo et al., 2021) proposed to learn the gradient of the log-likelihood w.r.t coordinates.”
Torsional Diffusion for Molecular Conformer Generation (2022, background): “A number of machine learning methods for conformer generation has been developed [Xu et al., 2021a,b, Shi et al., 2021, Luo et al., 2021], the most recent and advanced of which are GeoMol [Ganea et al., 2021] and GeoDiff [Xu et al., 2022].”
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, background): “DGSM [169] extends it to take into account the non-bonded interactions by making the molecular graph structure learnable [200].”
Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Graph Neural Networks [85, 251, 266, 285] and corresponding representation learning [86] techniques have achieved great success [14, 231, 250, 258, 264, 288] in many areas, including modeling molecule graph in various tasks ranging from property prediction [59, 71] to molecule generation [105, 111, ”
Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, background): “GraphDG [46], CGCF[47], ConfVAE [57], ConfGF [55], and DGSM [58] combine generative models with distance geometry, which first generates interatomic distance matrices and then iteratively generates atomic coordinates.”
DDGR: Continual Learning with Deep Diffusion-based Generative Replay (2023, background): “…have shown their exquisite potential in the field of computer vision (Dhariwal & Nichol, 2021; Ho et al., 2022b; Li et al., 2022; Luo & Hu, 2021), audio processing (Chen et al., 2021; Popov et al., 2021; Kim et al., 2022), and AI for science (Luo et al., 2021; Xu et al., 2022; Jing et al., 2022).”
Equivariant Diffusion for Molecule Generation in 3D (2022, methodology): “In particular, they have been integrated into autoregressive models (Gebauer et al., 2018; 2019) troduce an order in the atoms and are known to be difﬁcult to scale during sampling (Xu et al., 2021b).”