ArticleChemical science2023
SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- Q-GEM: Quantum Chemistry Knowledge Fusion Geometry-Enhanced Molecular Representation for Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Token-Mol 1.0: tokenized drug design with large language models.Nature communications · 2025Article
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- Diffusion Models in De Novo Drug Design.Journal of chemical information and modeling · 2024Review
- EC-Conf: A ultra-fast diffusion model for molecular conformation generation with equivariant consistency.Journal of cheminformatics · 2024Article
- COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework.Journal of chemical information and modeling · 2024Article
- QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning.Journal of cheminformatics · 2024Article
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generation of representative conformations for small molecules is a fundamental task in cheminformatics and computer-aided drug discovery, but capturing the complex distribution of conformations that contains multiple low energy minima is still a great challenge. Deep generative modeling, aiming to learn complex data distributions, is a promising approach to tackle the conformation generation problem. Here, inspired by stochastic dynamics and recent advances in generative modeling, we developed SDEGen, a novel conformation generation model based on stochastic differential equations. Compared with existing conformation generation methods, it enjoys the following advantages: (1) high model capacity to capture multimodal conformation distribution, thereby searching for multiple low-energy conformations of a molecule quickly, (2) higher conformation generation efficiency, almost ten times faster than the state-of-the-art score-based model, ConfGF, and (3) a clear physical interpretation to learn how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls in low energy minima. Extensive experiments demonstrate that SDEGen has surpassed existing methods in different tasks for conformation generation, interatomic distance distribution prediction, and thermodynamic property estimation, showing great potential for real-world applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.