Graph-Convolutional Deep Learning to Identify Optimized Molecular
Configurations
- URL: http://arxiv.org/abs/2108.09637v1
- Date: Sun, 22 Aug 2021 05:09:27 GMT
- Title: Graph-Convolutional Deep Learning to Identify Optimized Molecular
Configurations
- Authors: Eshan Joshi, Samuel Somuyiwa, and Hossein Z. Jooya
- Abstract summary: We implement a graph-convolutional method to classify molecular structures using the equilibrium and non-equilibrium configurations provided in the QM7-X data set.
We demonstrate the results using two different graph pooling layers and compare their respective performances.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Tackling molecular optimization problems using conventional computational
methods is challenging, because the determination of the optimized
configuration is known to be an NP-hard problem. Recently, there has been
increasing interest in applying different deep-learning techniques to benchmark
molecular optimization tasks. In this work, we implement a graph-convolutional
method to classify molecular structures using the equilibrium and
non-equilibrium configurations provided in the QM7-X data set. Atomic forces
are encoded in graph vertices and the substantial suppression in the total
force magnitude on the atoms in the optimized structure is learned for the
graph classification task. We demonstrate the results using two different graph
pooling layers and compare their respective performances.
Related papers
- Towards molecular docking with neutral atoms [0.0]
We map the molecular docking problem to a graph problem, a maximum-weight independent set problem on a unit-disk graph in a physical neutral atom quantum processor.
Results for multiple graphs are presented, and a small instance of the molecular docking problem is solved.
arXiv Detail & Related papers (2024-02-09T20:13:55Z) - Isotropic Gaussian Processes on Finite Spaces of Graphs [71.26737403006778]
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs.
We go further to consider sets of equivalence classes of unweighted graphs and define the appropriate versions of priors thereon.
Inspired by applications in chemistry, we illustrate the proposed techniques on a real molecular property prediction task in the small data regime.
arXiv Detail & Related papers (2022-11-03T10:18:17Z) - An Empirical Evaluation of Zeroth-Order Optimization Methods on
AI-driven Molecule Optimization [78.36413169647408]
We study the effectiveness of various ZO optimization methods for optimizing molecular objectives.
We show the advantages of ZO sign-based gradient descent (ZO-signGD)
We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
arXiv Detail & Related papers (2022-10-27T01:58:10Z) - Molecular Graph Generation via Geometric Scattering [7.796917261490019]
Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery.
We propose a representation-first approach to molecular graph generation.
We show that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis.
arXiv Detail & Related papers (2021-10-12T18:00:23Z) - Differentiable Scaffolding Tree for Molecular Optimization [47.447362691543304]
We propose differentiable scaffolding tree (DST) that utilizes a learned knowledge network to convert discrete chemical structures to locally differentiable ones.
Our empirical studies show the gradient-based molecular optimizations are both effective and sample efficient.
arXiv Detail & Related papers (2021-09-22T01:16:22Z) - GraphPiece: Efficiently Generating High-Quality Molecular Graph with
Substructures [7.021635649909492]
We propose a method to automatically discover common substructures, which we call em graph pieces, from given molecular graphs.
Based on graph pieces, we leverage a variational autoencoder to generate molecules in two phases: piece-level graph generation followed by bond completion.
arXiv Detail & Related papers (2021-06-29T05:26:18Z) - DAGs with No Curl: An Efficient DAG Structure Learning Approach [62.885572432958504]
Recently directed acyclic graph (DAG) structure learning is formulated as a constrained continuous optimization problem with continuous acyclicity constraints.
We propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly.
We show that our method provides comparable accuracy but better efficiency than baseline DAG structure learning methods on both linear and generalized structural equation models.
arXiv Detail & Related papers (2021-06-14T07:11:36Z) - A Bi-Level Framework for Learning to Solve Combinatorial Optimization on
Graphs [91.07247251502564]
We propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph.
Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity.
arXiv Detail & Related papers (2021-06-09T09:18:18Z) - 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) - Multilayer Clustered Graph Learning [66.94201299553336]
We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph.
Experiments show that our method leads to a clustered clusters w.r.t.
We learn a clustering algorithm for solving clustering problems.
arXiv Detail & Related papers (2020-10-29T09:58:02Z) - Graph Polish: A Novel Graph Generation Paradigm for Molecular
Optimization [7.1696593196695035]
We present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language" task.
We propose an effective and efficient learning framework T&S polish to capture the long-term dependencies in the optimization steps.
arXiv Detail & Related papers (2020-08-14T08:36:13Z)
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.