The discovery of new medications in a cost-effective manner has become the top priority for many pharmaceutical companies. Despite decades of innovation, many of their processes arguably remain relatively inefficient. One such process is the prediction of biological activity. This paper describes a new deep learning model, capable of conducting a preliminary screening of chemical compounds in-silico. The model has been constructed using a variation autoencoder to generate chemical compound fingerprints, which have been used to create a regression model to predict their LogD property and a classification model to predict binding in selected assays from the ChEMBL dataset. The conducted experiments demonstrate accurate prediction of the properties of chemical compounds only using structural definitions and also provide several opportunities to improve upon this model in the future.
|Number of pages||22|
|Journal||Neural Computing and Applications|
|Early online date||4 Jun 2021|
|Publication status||Published (in print/issue) - 31 Oct 2021|
Bibliographical noteFunding Information:
We acknowledge the contribution of Chris Swain the Founded Cambridge MedChem Consulting.
© 2021, The Author(s).
- Deep neural networks
- Machine learning
- chemical compounds Properties