Accelerating Drug Discovery with AI for a Biotech Innovator
When one of our biotech clients came to us, their research team was under intense pressure: reduce drug discovery time without sacrificing accuracy or compliance. Like many growing biotech companies, they were juggling limited lab resources, high costs, and the sheer volume of compounds that needed testing. Every decision counted — and every delay meant a slower path to helping patients.
The challenge
Their R&D team was spending months manually screening thousands of molecules, only to see low hit rates in early trials. They knew AI could help — but they needed a practical, trusted partner to turn that vision into something real.
The goal was clear:
Find a way to prioritize which chemical compounds to move forward into the lab, so scientists could focus on the most promising candidates faster.
Our approach
We worked hand-in-hand with the client’s scientists and data team to co-develop an AI-powered virtual screening platform tailored to their discovery pipeline.
Our team designed a custom machine learning system that could learn from the company’s own historical assay data and chemical structures, predicting which compounds were most likely to succeed.
Key elements included:
A data preprocessing engine to clean and encode molecular descriptors.
A deep learning model built on graph neural networks (GNNs) that scored each compound’s bioactivity.
A ranking dashboard that highlighted the top candidates for wet lab validation.
Integration with existing LIMS and ELN systems, so scientists could track predictions and lab outcomes in real time.
We didn’t stop at deployment. Together with the client’s research team, we built a continuous feedback loop — every new experiment helped retrain and refine the AI, making it smarter over time.
The impact
Within months, the results spoke for themselves:
60% faster screening by deprioritizing low-potential compounds before lab testing.
35% higher hit rate, helping the team identify more viable candidates with fewer trials.
A scalable architecture that now supports multiple therapeutic areas.
Closer collaboration between data scientists and chemists through shared AI-driven insights.
What started as an experiment quickly became a cornerstone of their R&D workflow. By embedding AI directly into early discovery, the company gained the ability to make faster, smarter go/no-go decisions, saving time, money, and — ultimately — helping bring therapies to patients sooner.
Ready to accelerate your own discovery workflows with AI?
Let’s talk about how LLMPerfected can help your team move from data to discovery faster.