Neural Network

Dec

I've previously written about molecular generators based on long short-term memory recurrent neural networks (LSTM-RNNs). The networks learn rules about how SMILES strings of molecules are formatted and are then able to create novel SMILES following the same rules by iterating through the characters. The results are "creative" computers that can ...

Dec

The SMILES enumeration code at GitHub has been revamped and revised into an object for easier use. It can work in conjunction with a SMILES iterator object that give on-the-fly enumeration and vectorization for training of SMILES based Recurrent Neural Network (RNN) models of molecules for ...

Nov

The film Inception with Leonardo Di Caprio is about dreams in dreams, and gave rise to the meme "We need to go deeper". The title has also given name to the Inception networks used by Google in their Inception network. I recently stumbled across two interesting ...

May

A few weeks ago I completed the 16 week "Neural Networks for Machine Learning" on-line course from University of Montreal to deepen my understanding of Artificial Neural Networks and their fascinating properties. The course is taught by Professor, Geoffrey Hinton, which is one of the grand old men in ...

May

I was recently contacted by Gaurav Chaudhary, who is the CEO of Roots Analysis,  a firm that provide market research and consulting for the pharmaceutical industry. It turns out that Wildcard Pharmaceutical Consulting was mentioned in their report: Deep Learning in Drug Discovery and Diagnostics, 2017 ...

Mar

The process of expanding an otherwise limited dataset in order to more efficiently train a neural network is known as Data Augmentation For images there have been used a variety of techniques, such as flipping, rotation, sub-segmenting and cropping, zooming. The mirror image of a cat is ...

Mar

Deep Neural Networks and Spectroscopic Data - a chemometric study A Masters project is available in a collaboration between Wildcard Pharmaceutical Consulting and Department of Food, Spectroscopy and Chemometrics, KU. We are looking for a motivated student with interest in spectroscopy and computer programming to take on a project involving ...

Feb

In another blog post I demonstrated how to build a deep neural network with Keras in Python to model some toxicity dataset from the Tox21 challenge. The network was however not systematically optimized, but merely put together with a few manually selected hyper parameters. Hyper parameters are understood as the parameters which are not tuned by the data ...

Jan

I found some interesting toxicology datasets from the Tox21 challenge, and wanted to see if it was possible to build a toxicology predictor using a deep neural network. I don't know how many layers a neural network actually has to have to be called "deep", but its a buzz word, so ...

Jan

In the last blogpost the battle tested principal components analysis (PCA) was used as a dimensionality reduction tool. This time we'll take a deeper look into chemical space by using a deep learning neural autoencoder, by testing some of the newer tools based on neural networks which has shown promising results. ...

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