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Showing posts from May, 2019

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications:

- Deep Learning as an Opportunity in VirtualScreening
- Massively Multitask Networks for Drug Discovery
- Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach.

So, having a set of activities relating targets and molecules we can train a single neural network as a binary multi-label classifier that will output the probability of activity/inactivity for each of the targets (tasks) for a given query molecule…