Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search

• URL: http://arxiv.org/abs/2108.10266v1
• Date: Mon, 23 Aug 2021 16:10:57 GMT
• Title: Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search
• Authors: Naveed Ahmed Azam, Jianshen Zhu, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi and Tatsuya Akutsu
• Abstract summary: In this paper, we propose a procedure for generating other feasible solutions of a mixed integer linear program. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.
• Score: 6.519339570726759
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.

Related papers

• GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned. We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• MolGrapher: Graph-based Visual Recognition of Chemical Structures [50.13749978547401]
  We introduce MolGrapher to recognize chemical structures visually. We treat all candidate atoms and bonds as nodes and put them in a graph. We classify atom and bond nodes in the graph with a Graph Neural Network.
  arXiv  Detail & Related papers  (2023-08-23T16:16:11Z)
• Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model [5.845754795753478]
  The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP. The results of our computational experiments suggest that the proposed method can infer a chemical structure with up to 50 non-hydrogen atoms.
  arXiv  Detail & Related papers  (2022-09-13T08:27:25Z)
• Retrieval-based Controllable Molecule Generation [63.44583084888342]
  We propose a new retrieval-based framework for controllable molecule generation. We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
  arXiv  Detail & Related papers  (2022-08-23T17:01:16Z)
• Molecular graph generation with Graph Neural Networks [2.7393821783237184]
  We introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG2N2. Our model is capable of generalizing molecular patterns seen during the training phase, without overfitting.
  arXiv  Detail & Related papers  (2020-12-14T10:32:57Z)
• Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions [79.45090959869124]
  We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions.
  arXiv  Detail & Related papers  (2020-07-08T17:21:00Z)
• Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits [0.0]
  We present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large datasets.
  arXiv  Detail & Related papers  (2020-06-27T18:50:24Z)
• Multipole Graph Neural Operator for Parametric Partial Differential Equations [57.90284928158383]
  One of the main challenges in using deep learning-based methods for simulating physical systems is formulating physics-based data. We propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time.
  arXiv  Detail & Related papers  (2020-06-16T21:56:22Z)
• Retrosynthesis Prediction with Conditional Graph Logic Network [118.70437805407728]
  Computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. We propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks.
  arXiv  Detail & Related papers  (2020-01-06T05:36:57Z)

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.