Hyperparameter Search With GPyOpt: Part 3 – Keras (CNN) Classification and Ensembling

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling.

In this article, we demonstrate how to use this package to perform hyperparameter search for a classification problem with Keras.

Hyperparameter Search With GPyOpt: Part 2 – XGBoost Classification and Ensembling

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling.

In this article, we demonstrate how to use this package to perform hyperparameter search for a classification problem with XGBoost.

Paper: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery – Xu et al 2017

In this paper, we propose a novel unsupervised molecular embedding method, providing a continuous feature vector for each molecule to perform further tasks, e.g., solubility classification. In the proposed method, a multi-layered Gated Recurrent Unit (GRU) network is used to map the input molecule into a continuous feature vector of fixed dimensionality, and then another deep GRU network is employed to decode the continuous vector back to the original molecule.

Paper: CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations – Paul et al 2018

In this interesting paper, the authors use a multi input neural network to predict various small molecule properties in which one branch is a multilayer perceptron with MACCS fingerprints and the other is one of a RNN, 1D CNN, 1D CNN-RNN with SMILES.

Paper: DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures – O’Boyle and Dalke 2018

SMILES (Simplified Molecular Input Line Entry System) representations of molecules have found many uses in machine learning algorithms, especially those derived from natural language processing techniques. However, they were not designed for machine learning and thus suffer from various syntax issues that can hamper machine learning methods, especially generative methods. DeepSMILES is a modification of SMILES explicitly designed to address these issues.

Paper: Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space – Nigam et al 2020

In this paper, the authors use a genetic algorithm operating on the SELFIES (SELF-referencIng Embedded Strings) representation of molecules to explore the vast space of small molecules. A neural network is used to guide the exploration process. Also, fitness functions are constructed to generate molecules with specific properties.

Paper: Auto-Keras: An Efficient Neural Architecture Search System – Jin et al. 2019

Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space.

Paper: MolGAN: An implicit generative model for small molecular graphs – De Cao and Kipf 2018

We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties.

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