The development of new medicines is a complex, resource-intensive process with a high failure rate. Leveraging artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize drug discovery by enhancing data analysis and prediction, leading to faster and more effective treatments.
The process of developing new medicines is complex and resource intensive, with a high failure rate. Across the industry, approximately 90% of drug candidates fail in preclinical or clinical trials, and it can take more than ten years to determine their effectiveness. The sheer scale and complexity of the scientific data involved in drug discovery pose significant barriers to progress. Computational approaches have enhanced data collection and analysis, but have historically not matched the magnitude of this problem. Thus, there’s still potential for further advancements in the faster delivery of new medicines and improved success rates in research.
The ‘lab in a loop’ is a mechanism by which you bring generative AI to drug discovery and development.
Genentech, a member of the Roche Group, has reached an inflection point where artificial intelligence (AI) and machine learning (ML) are leveraged to redefine the drug discovery process. “The ‘lab in a loop’ is a mechanism by which you bring generative AI to drug discovery and development,” says Aviv Regev, Head of Genentech Research and Early Development (gRED). It means that data from the lab and clinic are used to train AI models and algorithms designed by their researchers, and then the trained models are used to make predictions on drug targets, therapeutic molecules and more. Those predictions are tested in the lab, generating new data that also helps retrain the models to be even more accurate. This streamlines the traditional trial-and-error approach for novel therapies and improves the performance of the models across all programmes.
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The ‘lab in a loop’ strategy involves training AI models with massive quantities of data generated from lab experiments and clinical studies. These models generate predictions about disease targets and designs of potential medicines that are experimentally tested by our scientists in the lab.
Impact on cancer vaccines and beyond
By using AI approaches, we can select the most promising neoantigens (proteins generated by tumour-specific mutations) for cancer vaccines, hopefully leading to more effective treatments for individual patients. AI and ML also enable the rapid generation and testing of virtual structures for thousands of new molecules and the simulation of their interactions with therapeutic targets. AI strategies are being deployed to optimise antibody design, predict small-molecule activity, identify new antibiotic compounds and explore new disease indications for investigational therapies.
The process of developing new medicines is complex and resource intensive, with a high failure rate. Across the industry, approximately 90% of drug candidates fail in preclinical or clinical trials, and it can take more than ten years to determine their effectiveness. The sheer scale and complexity of the scientific data involved in drug discovery pose significant barriers to progress. Computational approaches have enhanced data collection and analysis, but have historically not matched the magnitude of this problem. Thus, there’s still potential for further advancements in the faster delivery of new medicines and improved success rates in research.
The ‘lab in a loop’ is a mechanism by which you bring generative AI to drug discovery and development.
Genentech, a member of the Roche Group, has reached an inflection point where artificial intelligence (AI) and machine learning (ML) are leveraged to redefine the drug discovery process. “The ‘lab in a loop’ is a mechanism by which you bring generative AI to drug discovery and development,” says Aviv Regev, Head of Genentech Research and Early Development (gRED). It means that data from the lab and clinic are used to train AI models and algorithms designed by their researchers, and then the trained models are used to make predictions on drug targets, therapeutic molecules and more. Those predictions are tested in the lab, generating new data that also helps retrain the models to be even more accurate. This streamlines the traditional trial-and-error approach for novel therapies and improves the performance of the models across all programmes.
The ‘lab in a loop’ strategy involves training AI models with massive quantities of data generated from lab experiments and clinical studies. These models generate predictions about disease targets and designs of potential medicines that are experimentally tested by our scientists in the lab.
Impact on cancer vaccines and beyond
By using AI approaches, we can select the most promising neoantigens (proteins generated by tumour-specific mutations) for cancer vaccines, hopefully leading to more effective treatments for individual patients. AI and ML also enable the rapid generation and testing of virtual structures for thousands of new molecules and the simulation of their interactions with therapeutic targets. AI strategies are being deployed to optimise antibody design, predict small-molecule activity, identify new antibiotic compounds and explore new disease indications for investigational therapies.
Enhancing capabilities through collaborations
Utilising AI in drug discovery requires increasingly powerful computing capabilities to process the growing amount of data and train algorithms. In order to address this, Roche is collaborating with leading technology companies like AWS and NVIDIA. “To take advantage of these new approaches and to apply them rapidly, we need to bring together expertise from different disciplines - by doing so we have a tremendous opportunity to hopefully bring medicines to patients faster than we do today,” says John Marioni, Senior Vice President and Head of Computational Sciences at Genentech. With NVIDIA we are collaborating to enhance our proprietary ML algorithms and models using accelerated computing and software, ultimately speeding up the drug development process and improving the success rate of research and development
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