Energy-based Generative Models for Target-specific Drug Discovery
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Abstract (arXiv)
Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis. Computational approaches are widely applied to drug development because of the increasing availability of biological molecular datasets. Popular generative approaches can create new drug molecules by learning the given molecule distributions. However, these approaches are mostly not for target-specific drug discovery. We developed an energy-based probabilistic model for computational target-specific drug discovery. Results show that our proposed TagMol can generate molecules with similar binding affinity scores as real molecules. GAT-based models showed faster and better learning relative to GCN baseline models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 5 Dec 2022.
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12 of 27 references.
References · 12
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2022Generative Adversarial Networkscited by 5,627
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2018MolGAN: An implicit generative model for small molecular graphscited by 393
- 2017HP-GAN: Probabilistic 3D human motion prediction via GANcited by 176
- 2019On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Modelscited by 105
- 2019Relational Graph Attention Networkscited by 82
- 2021Quantum Generative Models for Small Molecule Drug Discoverycited by 37
- 2021Relation-aware Graph Attention Model With Adaptive Self-adversarial Trainingcited by 12
- 2021Scalable Variational Quantum Circuits for Autoencoder-based Drug Discoverycited by 6
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