MolGAN: An implicit generative model for small molecular graphs

Cao, Nicola De; Kipf, Thomas

doi:10.48550/arxiv.1805.11973

Cited by 158 publications

(244 citation statements)

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“…Recently, GNNs have been widely investigated in modeling chemical structures [59]. GNNs have been succesfully used on the molecular graphs to learn FPs [60,61], predict molecular properties [15][16][17]62], and generate target molecules [63][64][65]. Chemical structures, both organic molecules and inorganic structures, are represented as graphs.…”

Section: Graph Neural Network For Chemical Structuresmentioning

confidence: 99%

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

Magar¹,

Wang²,

Cooper³

et al. 2021

Preprint

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Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and Graph Neural Networks (GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures,

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Section: Graph Neural Network For Chemical Structuresmentioning

confidence: 99%

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

Magar¹,

Wang²,

Cooper³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recent machine learning approaches to molecule design have shown advantages over greedy approaches such as directed evolution. Generative autoencoders and GANs have been used to generate SMILES strings of molecules with specific properties [17] [18]. Deep Q-Learning was used to optimize SMILES strings for quantities such as logP and QED [19].…”

Section: Introductionmentioning

confidence: 99%

Improving RNA Secondary Structure Design using Deep Reinforcement Learning

Whatley¹,

Luo²,

Tang³

2021

Preprint

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Rising costs in recent years of developing new drugs and treatments have led to extensive research in optimization techniques in biomolecular design. Currently, the most widely used approach in biomolecular design is directed evolution, which is a greedy hill-climbing algorithm that simulates biological evolution. In this paper, we propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. In addition to experimenting with the vanilla implementations of each reinforcement learning algorithm from standard libraries, we analyze variants of each algorithm in which we modify the algorithm's reward function and tune the model's hyperparameters. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches and its ability to search the possible space of RNA sequences. We find that our DQN algorithm performs by far the best in this setting, contrasting with, in which PPO performs the best among all tested algorithms. Our results should be of interest to those in the biomolecular design community and should serve as a baseline for future experiments involving machine learning in molecule design.

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“…Graph neural networks (GNNs) have recently emerged as one the most popular machine learning models for processing and analyzing graph-structured data [1,2]. GNNs have gained significant and steady attention due to their extraordinary success in solving many challenging tasks in a variety of scientific disciplines such as computational pharmacology [3], molecular chemistry [4], physics [5], finance [6,7], wireless communications [8], and combinatorial optimization [9], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification

Doshi¹,

Chepuri²

2021

Preprint

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We focus on graph classification using a graph neural network (GNN) model that precomputes the node features using a bank of neighborhood aggregation graph operators arranged in parallel. These GNN models have a natural advantage of reduced training and inference time due to the precomputations but are also fundamentally different from popular GNN variants that update node features through a sequential neighborhood aggregation procedure during training. We provide theoretical conditions under which a generic GNN model with parallel neighborhood aggregations (PA-GNNs, in short) are provably as powerful as the well-known Weisfeiler-Lehman (WL) graph isomorphism test in discriminating non-isomorphic graphs. Although PA-GNN models do not have an apparent relationship with the WL test, we show that the graph embeddings obtained from these two methods are injectively related. We then propose a specialized PA-GNN model, called SPIN, which obeys the developed conditions. We demonstrate via numerical experiments that the developed model achieves state-of-the-art performance on many diverse real-world datasets while maintaining the discriminative power of the WL test and the computational advantage of preprocessing graphs before the training process.

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MolGAN: An implicit generative model for small molecular graphs

Cited by 158 publications

References 0 publications

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

Improving RNA Secondary Structure Design using Deep Reinforcement Learning

Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification

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