Docking-based generative approaches in the search for new drug candidates
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Abstract (arXiv)
Despite the great popularity of virtual screening of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design. In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design. In addition, we discuss the most promising directions for further development of generative protocols coupled with docking.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 59 references that are in the map. First posted on arXiv 22 Nov 2023.
Builds on
11 of 59 references.
References · 11
- 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
- 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 264
- 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
- 2022A 3D Generative Model for Structure-Based Drug Designcited by 148
- 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
- 2021Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Modelscited by 72
- 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
- 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 55
- 2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
- 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
- 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.