AI Drug Discovery Platforms are the new wave in drug development. These platforms combine computerised technologies to speed up the process of finding novel drugs and providing personalised care for patients.
The drug discovery process is a long one. Before a medicine can be made, sold, or tested, the hit compound must first be found. The selection process is every bit as important as its optimization.
Pharmaceutical companies are increasingly partnering with AI-powered biotech companies, Contract Research Organisations, and AI-drug discovery platforms. The most successful outcomes arise when this state-of-the-art technology is combined with high-quality data.
Choosing the right AI drug discovery platform is critical
The stages in drug discovery begins as a research hypothesis: that a drug with a specific effect — such as modulating a specified protein channel — will be beneficial in the treatment of some disease. The search begins. And so, Guido was lucky to find one with the right effect. But fundamentally, finding that compound can take far more time and resources than most of us have. So choosing the right platform to bridge the gap is critical.
In drug discovery, advances in machine learning and data have revolutionised entire fields. To truly maximise their impact, approaches are needed that adequately address the complexity of patterns observed in the lab, in animal models, and hopefully in humans. Most off-the-shelf approaches have made high-profile impacts in image recognition; however, these approaches are seldom relevant in spaces where the data is often multifaceted, not well understood, expensive to collect and highly biassed. In essence recognizing a cat in a photo is fundamentally different than winning at go, designing a drug is fundamentally different than recognizing high level features of atoms or molecules.
In recent years, machine learning has helped scientists better predict the activity of molecules in various biological systems. While machine learning has been used in drug discovery before, it is only recently that the technology and data have caught up. “Models are better, algorithms are better, better datasets, better computational resources…We’re really just starting to see the potential of that on drug discovery projects now, and it’s going to play a big part of what we do in the future,”
AI-Enabled Drug Discovery Solutions
AI drug discovery platforms, such as Valo and others, can now be used by CRO services to offer services that are far more efficient. By integrating their laboratory capabilities with Valo’s AI platform, they have demonstrated the ability to produce advanceable lead series at cost-effectiveness exceeding 90%.
AI is a tool that can also be used as an augmentation for, or instead of, humans. The use of AI in drug discovery has already begun and will continue to reach new levels of sophistication and accuracy. There’s no doubt that we’ll see this technology applied to human problems, from medical diagnosis to life-saving treatment, but for now, the focus remains on finding cures for novel diseases.
Lanza’s project is looking to identify compounds with similar biological properties and targets as the original drug. He started by creating a model that allowed him to identify all compounds in the data set with similar pharmacological profiles. This was then used with a separate model that identified any compound that has ever gotten into the brain.
