BioLizard first joined forces with Madam Therapeutics to develop machine learning models to predict potential efficacy and toxicity of so-called Synthetic Anti-Microbial and Anti-Biofilm Peptides (SAAPs). Alongside building these predictive models, BioLizard’s teams also created a database of toxicity and activity of antimicrobial peptides (AMPs) using publicly available data and information extracted from scientific literature. They did this using a variety of different natural language processing techniques – the same family of computational techniques used in by a popular chatbot that you might have heard of: ChatGTP. In the case of this database created by BioLizard’s teams, the data contained within it could then be used for the training and testing of the sequence-based prediction models.
From there, Madam Therapeutics was able to select a set of promising potential AMPs for experimental validation. After these experiments were performed, it was time for BioLizard to provide follow-up bioinformatics support. The new aim? To use the new experimental results generated by Madam Therapeutics to further optimise the machine learning prediction models for AMP toxicity and antimicrobial activity, and also to create separate predictive models for specific bacterial species and genera.
In the end, the algorithms created by BioLizard’s Bioinformatics team generated almost 100 million novel AMP sequences. Using our improved and more specific machine learning models, we were able to predict the toxicity and anti-microbial activity of every single one of these sequences. This provided a rich set of promising potential new AMPs for Madam Therapeutics to select and further test.
This collaboration between BioLizard’s Bioinformatics team and Madam Therapeutics is a great example of how bioinformatics and machine learning approaches can be used to fine-tune challenging drug development pipelines, and how computational tools can be used iteratively in combination with experimental data.