A Systematic Survey in Geometric Deep Learning for Structure-based Drug
Design
- URL: http://arxiv.org/abs/2306.11768v5
- Date: Tue, 24 Oct 2023 14:22:59 GMT
- Title: A Systematic Survey in Geometric Deep Learning for Structure-based Drug
Design
- Authors: Zaixi Zhang, Jiaxian Yan, Qi Liu, Enhong Chen, and Marinka Zitnik
- Abstract summary: Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to identify potential drug candidates.
Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, have greatly advanced the field of structure-based drug design.
- Score: 63.30166298698985
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Structure-based drug design (SBDD) utilizes the three-dimensional geometry of
proteins to identify potential drug candidates. Traditional methods, grounded
in physicochemical modeling and informed by domain expertise, are
resource-intensive. Recent developments in geometric deep learning, focusing on
the integration and processing of 3D geometric data, coupled with the
availability of accurate protein 3D structure predictions from tools like
AlphaFold, have greatly advanced the field of structure-based drug design. This
paper systematically reviews the current state of geometric deep learning in
SBDD. We first outline foundational tasks in SBDD, detail prevalent 3D protein
representations, and highlight representative predictive and generative models.
We then offer in-depth reviews of each key task, including binding site
prediction, binding pose generation, \emph{de novo} molecule generation, linker
design, and binding affinity prediction. We provide formal problem definitions
and outline each task's representative methods, datasets, evaluation metrics,
and performance benchmarks. Finally, we summarize the current challenges and
future opportunities: current challenges in SBDD include oversimplified problem
formulations, inadequate out-of-distribution generalization, a lack of reliable
evaluation metrics and large-scale benchmarks, and the need for experimental
verification and enhanced model understanding; opportunities include leveraging
multimodal datasets, integrating domain knowledge, building comprehensive
benchmarks, designing criteria based on clinical endpoints, and developing
foundation models that broaden the range of design tasks. We also curate
\url{https://github.com/zaixizhang/Awesome-SBDD}, reflecting ongoing
contributions and new datasets in SBDD.
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