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Paper: A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility – Tang et al 2020

by GCBC Ventures | Feb 28, 2020 | Machine Learning, Papers

A new message passing graph neural network with attention is presented here as applied to small molecule predictive machine learning tasks. In this paper, we describe a self-attention-based message-passing neural network (SAMPN) model, which is a modification of...

Paper: A Deep Learning Approach to Antibiotic Discovery – Stokes et al 2020

by GCBC Ventures | Feb 21, 2020 | Machine Learning, Papers

This is a very interesting paper in which the authors use a message passing neural network on a carefully selected data set to predict antibacterial activity against E. coli. Then they apply their model to other data sets, while also prioritizing molecules that are...

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

by GCBC Ventures | Feb 15, 2020 | Machine Learning, Papers

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...

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

by GCBC Ventures | Feb 13, 2020 | Machine Learning, Papers

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...

Paper: Optuna: A Next-generation Hyperparameter Optimization Framework – Akiba et al 2019

by GCBC Ventures | Feb 11, 2020 | Machine Learning, Papers

This paper introduces Optuna, a Python package for performing hyperparameter optimization and pruning for machine learning algorithms. It is the creation of Preferred Networks who have kindly released what had been an internal project as open source code. This should...

Paper: Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar – Drori et al 2019

by GCBC Ventures | Jan 19, 2020 | Machine Learning, Papers

In this paper, the authors demonstrate a feasible and promising approach to automatically constructing machine learning pipelines. We formulate the AutoML problem of pipeline synthesis as a single-player game, in which the player starts from an empty pipeline, and in...
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