Movatterモバイル変換


[0]ホーム

URL:


Skip to main content
Cornell University

Monday, May 5: arXiv will be READ ONLY at 9:00AM EST for approximately 30 minutes. We apologize for any inconvenience.

We gratefully acknowledge support from the Simons Foundation,member institutions, and all contributors.Donate
arxiv logo>physics> arXiv:2311.06295
arXiv logo
Cornell University Logo

Physics > Chemical Physics

arXiv:2311.06295 (physics)
[Submitted on 5 Nov 2023 (v1), last revised 12 Mar 2024 (this version, v2)]

Title:Gradual Optimization Learning for Conformational Energy Minimization

View PDFHTML (experimental)
Abstract:Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization methods that use molecular forces calculated by a physical simulator (oracle) as anti-gradients. However, this is a computationally expensive approach that requires many interactions with a physical simulator. One way to accelerate this procedure is to replace the physical simulator with a neural network. Despite recent progress in neural networks for molecular conformation energy prediction, such models are prone to distribution shift, leading to inaccurate energy minimization. We find that the quality of energy minimization with neural networks can be improved by providing optimization trajectories as additional training data. Still, it takes around $5 \times 10^5$ additional conformations to match the physical simulator's optimization quality. In this work, we present the Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks that significantly reduces the required additional data. The framework consists of an efficient data-collecting scheme and an external optimizer. The external optimizer utilizes gradients from the energy prediction model to generate optimization trajectories, and the data-collecting scheme selects additional training data to be processed by the physical simulator. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules using $50$x less additional data.
Comments:Published as a conference paper at ICLR2024 (Poster)
Subjects:Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
Cite as:arXiv:2311.06295 [physics.chem-ph]
 (orarXiv:2311.06295v2 [physics.chem-ph] for this version)
 https://doi.org/10.48550/arXiv.2311.06295
arXiv-issued DOI via DataCite

Submission history

From: Artem Tsypin [view email]
[v1] Sun, 5 Nov 2023 11:48:08 UTC (2,177 KB)
[v2] Tue, 12 Mar 2024 07:36:05 UTC (2,574 KB)
Full-text links:

Access Paper:

Current browse context:
physics.chem-ph
Change to browse by:
export BibTeX citation

Bookmark

BibSonomy logoReddit logo

Bibliographic and Citation Tools

Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
scite Smart Citations(What are Smart Citations?)

Code, Data and Media Associated with this Article

CatalyzeX Code Finder for Papers(What is CatalyzeX?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)

Demos

Hugging Face Spaces(What is Spaces?)

Recommenders and Search Tools

Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.

Which authors of this paper are endorsers? |Disable MathJax (What is MathJax?)

[8]ページ先頭

©2009-2025 Movatter.jp