Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Not on the map. Why?
Abstract (arXiv)
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability. The fragment-wise generation paradigm offers a promising solution. However, a common challenge across both atom-wise and fragment-wise methods lies in their limited ability to co-design plausible chemical and geometrical structures, resulting in distorted conformations. In response to this challenge, we introduce the Deep Geometry Handling protocol, a more abstract design that extends the design focus beyond the model architecture. Through a comprehensive review of existing geometry-related models and their protocols, we propose a novel hybrid strategy, culminating in the development of FragGen - a geometry-reliable, fragment-wise molecular generation method. FragGen marks a significant leap forward in the quality of generated geometry and the synthesis accessibility of molecules. The efficacy of FragGen is further validated by its successful application in designing type II kinase inhibitors at the nanomolar level.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 15 Mar 2024.
Builds on
9 of 37 references.
References · 9
- 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 310
- 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
- 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 214
- 2020Molecular graph generation with Graph Neural Networkscited by 79
- 2019A Generative Model for Molecular Distance Geometrycited by 73
- 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
- 2022Direct Molecular Conformation Generationcited by 30
- 2023PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embeddingcited by 7
- 2023Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?cited by 1
Built on by
Citing papers per year: 2024: 5.
5 papers.
- 2024The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challengescited by 29
- 2024ReactZyme: A Benchmark for Enzyme-Reaction Predictioncited by 13
- 2024EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamicscited by 5
- 2024Reaction-conditioned De Novo Enzyme Design with GENzymecited by 3
- 2024Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learningcited by 0
- 2024Reaction-conditioned De Novo Enzyme Design with GENzyme10 Nov 2024
- 2024Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning9 Nov 2024
- 2024EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics1 Oct 2024
- 2024ReactZyme: A Benchmark for Enzyme-Reaction Prediction24 Aug 2024
- 2024The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges12 Jul 2024