Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees

• URL: http://arxiv.org/abs/2407.09357v3
• Date: Tue, 15 Jul 2025 19:51:11 GMT
• Title: Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees
• Authors: Alexia Jolicoeur-Martineau, Aristide Baratin, Kisoo Kwon, Boris Knyazev, Yan Zhang,
• Abstract summary: Spanning Tree-based Graph Generation (STGG) is a promising approach to ensure generation of valid molecules.<n>In this work, we extend STGG to multi-property-conditional generation.<n>We show that STGG+ achieves state-of-the-art performance on in-distribution and out-of-distribution conditional generation.
• Score: 11.957429240197452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating novel molecules is challenging, with most representations leading to generative models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) is a promising approach to ensure the generation of valid molecules, outperforming state-of-the-art SMILES and graph diffusion models for unconditional generation. In the real world, we want to be able to generate molecules conditional on one or multiple desired properties rather than unconditionally. Thus, in this work, we extend STGG to multi-property-conditional generation. Our approach, STGG+, incorporates a modern Transformer architecture, random masking of properties during training (enabling conditioning on any subset of properties and classifier-free guidance), an auxiliary property-prediction loss (allowing the model to self-criticize molecules and select the best ones), and other improvements. We show that STGG+ achieves state-of-the-art performance on in-distribution and out-of-distribution conditional generation, and reward maximization.

Related papers

• Conditional Synthesis of 3D Molecules with Time Correction Sampler [58.0834973489875]
  Time-Aware Conditional Synthesis (TACS) is a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties.
  arXiv  Detail & Related papers  (2024-11-01T12:59:25Z)
• Geometric Representation Condition Improves Equivariant Molecule Generation [24.404588237915732]
  We introduce GeoRCG, a framework to enhance the performance of molecular generative models. We decompose the molecule generation process into two stages: first, generating an informative geometric representation; second, generating a molecule conditioned on the representation. We observe significant quality improvements in unconditional molecule generation tasks on the widely-used QM9 and GEOM-DRUG datasets.
  arXiv  Detail & Related papers  (2024-10-04T17:57:35Z)
• Molecule Design by Latent Prompt Transformer [76.2112075557233]
  This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task. We propose a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt.
  arXiv  Detail & Related papers  (2024-02-27T03:33:23Z)
• Feedback Efficient Online Fine-Tuning of Diffusion Models [52.170384048274364]
  We propose a novel reinforcement learning procedure that efficiently explores on the manifold of feasible samples. We present a theoretical analysis providing a regret guarantee, as well as empirical validation across three domains.
  arXiv  Detail & Related papers  (2024-02-26T07:24:32Z)
• A novel molecule generative model of VAE combined with Transformer for unseen structure generation [0.0]
  Transformer and VAE are widely used as powerful models, but they are rarely used in combination due to structural and performance mismatch. This study proposes a model that combines these two models through structural and parameter optimization in handling diverse molecules. The proposed model shows comparable performance to existing models in generating molecules, and showed by far superior performance in generating molecules with unseen structures.
  arXiv  Detail & Related papers  (2024-02-19T08:46:04Z)
• Zero Shot Molecular Generation via Similarity Kernels [0.6597195879147557]
  We present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation. SiMGen combines a time-dependent similarity kernel with descriptors from a pretrained machine learning force field to generate molecules. We also release an interactive web tool that allows users to generate structures with SiMGen online.
  arXiv  Detail & Related papers  (2024-02-13T17:53:44Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation [4.517805235253331]
  MolHF is a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. MolHF is the first flow-based model that can be applied to model larger molecules (polymer) with more than 100 heavy atoms.
  arXiv  Detail & Related papers  (2023-05-15T08:59:35Z)
• Exploring Chemical Space with Score-based Out-of-distribution Generation [57.15855198512551]
  We propose a score-based diffusion scheme that incorporates out-of-distribution control in the generative differential equation (SDE) Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool.
  arXiv  Detail & Related papers  (2022-06-06T06:17:11Z)
• Interpretable Molecular Graph Generation via Monotonic Constraints [19.401468196146336]
  Deep graph generative models treat molecule design as graph generation problems. Existing models have many shortcomings, including poor interpretability and controllability toward desired molecular properties. This paper proposes new methodologies for molecule generation with interpretable and deep controllable models.
  arXiv  Detail & Related papers  (2022-02-28T08:35:56Z)
• Learning Latent Space Energy-Based Prior Model for Molecule Generation [59.875533935578375]
  We learn latent space energy-based prior model with SMILES representation for molecule modeling. Our method is able to generate molecules with validity and uniqueness competitive with state-of-the-art models.
  arXiv  Detail & Related papers  (2020-10-19T09:34:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.