MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder

• URL: http://arxiv.org/abs/2202.07476v1
• Date: Mon, 14 Feb 2022 14:33:33 GMT
• Title: MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder
• Authors: Myeonghun Lee and Kyoungmin Min
• Abstract summary: This study proposes a molecular graph generative model based on the autoencoder for de novo design. Results: Among generated molecules, 25.89% optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The ultimate goal of various fields is to directly generate molecules with desired properties, such as finding water-soluble molecules in drug development and finding molecules suitable for organic light-emitting diode (OLED) or photosensitizers in the field of development of new organic materials. In this respect, this study proposes a molecular graph generative model based on the autoencoder for de novo design. The performance of molecular graph conditional variational autoencoder (MGCVAE) for generating molecules having specific desired properties is investigated by comparing it to molecular graph variational autoencoder (MGVAE). Furthermore, multi-objective optimization for MGCVAE was applied to satisfy two selected properties simultaneously. In this study, two physical properties -- logP and molar refractivity -- were used as optimization targets for the purpose of designing de novo molecules, especially in drug discovery. As a result, it was confirmed that among generated molecules, 25.89% optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE. Hence, it demonstrates that MGCVAE effectively produced drug-like molecules with two target properties. The results of this study suggest that these graph-based data-driven models are one of the effective methods of designing new molecules that fulfill various physical properties, such as drug discovery.

Related papers

• MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures [2.5563339057415218]
  MolIG is a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. It amalgamates the strengths of both molecular representation forms. It exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups.
  arXiv  Detail & Related papers  (2023-11-28T10:28:35Z)
• Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties [33.2976176283611]
  We present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs. To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method. We show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
  arXiv  Detail & Related papers  (2023-10-05T11:43:21Z)
• 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)
• An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [54.92529253182004]
  We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
  arXiv  Detail & Related papers  (2023-04-12T13:34:22Z)
• 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)
• Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
  We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T06:10:22Z)
• Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations [21.237758981760784]
  We present a SMILES Variational Auto-Encoder (VAE) augmented with topological data analysis (TDA) representations of molecules. Our experiments show that this TDA augmentation enables a SMILES VAE to capture the complex relation between 3D geometry and electronic properties.
  arXiv  Detail & Related papers  (2021-06-08T15:49:21Z)
• Reinforced Molecular Optimization with Neighborhood-Controlled Grammars [63.84003497770347]
  We propose MNCE-RL, a graph convolutional policy network for molecular optimization. We extend the original neighborhood-controlled embedding grammars to make them applicable to molecular graph generation. We show that our approach achieves state-of-the-art performance in a diverse range of molecular optimization tasks.
  arXiv  Detail & Related papers  (2020-11-14T05:42:15Z)
• MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization [51.00815310242277]
  generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties. We propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution.
  arXiv  Detail & Related papers  (2020-10-05T20:18:42Z)
• Conditional Constrained Graph Variational Autoencoders for Molecule Design [70.59828655929194]
  We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model. We show improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.
  arXiv  Detail & Related papers  (2020-09-01T21:58:07Z)

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.