Turning complex biomedical data into actionable insights that accelerate discovery and impact.
>
Services
Delivering tailored analytics and AI solutions that turn scientific data into decisions.
>
Development journey
We follow your biology from early discovery to clinical phases and post-marketing.
>
Discover
Bio|Verse®
5
About us
Join a biology-driven data science team shaping innovation across pharma and biotech.
>
Careers
Join a biology-driven data science team shaping innovation across pharma and biotech.
>
Case studies
Actionable examples of our work. >
News
Discover what our Lizards have been up to. >
Let’s discuss how we can turn your data into real scientific impact.
Contact us >
Dialog window
Development journey
Services
Bio|Verse®
Case studies
About
Careers
News
Let’s discuss how we can turn your data into real scientific impact.
Contact us >
Using bioinformatics and machine learning to find new anti-microbial therapeutics
BioLizard partnered with Madam Therapeutics to harness bioinformatics and machine learning for discovering new antimicrobial peptides. By integrating experimental data with predictive models, they generated nearly 100 million novel sequences, accelerating drug development pipelines and optimizing candidate selection.
Antimicrobial resistance is on the rise, but the amount of new antibiotics released to the market each year is notoriously low. But there’s good news, too: harnessing the power of bioinformatics to find new and promising potential treatments for bacterial infections can dramatically speed up product development pipelines, to bring new potential treatments to market more quickly. Today, let’s dive into a client case that demonstrates exactly how that can be done.
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.
BioLizard was able to successfully integrate the new experimental activity and toxicity data with the data from the existing database, and trained new species- and genera-specific predictive models that had improved performance! In addition, in an interactive approach, the Bioinformatics team also created brand-new synthetic sequence libraries, based on a set of rules defined by Madam Therapeutics, and ran the predictive models on all the sequences in these libraries in order to find more new and promising AMPs. 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.
In oncology, the drug development path is unique: Phase 0 and Phase I trials are typically conducted in patients rather than healthy volunteers, allowing for early assessment of efficacy and patient selection alongside safety. Yet, even with this early clinical insight, many cancer drugs show promise in the lab but fail to transition effectively into the clinic. This often happens because, while we verify that a drug’s target is present, we frequently overlook its context, specifically its location, the surrounding microenvironment, and its interaction with neighboring cells. By revisiting real-world examples of discontinued trials, this post explains why understanding the “where” is just as critical as the “what”, and how spatial biology is positioning itself as a valuable avenue for validating clinical potential.
BioLizard has been named “Bioinformatics Innovation of the Year” in the 2025 BioTech Breakthrough Awards. The honor spotlights Bio|Verse® Navigator, a visual analytics workspace that unifies complex R&D data so scientists can decide faster, with confidence.
Bioinformatics pipelines break for the smallest reasons: package updates, shifting dependencies, or “it only works on my machine.” This post explains why experienced software engineers and DevOps practices (Git, CI/CD, IaC) are essential to keep workflows reproducible, stable, and scalable.
