Model Deployment Booster for ML Drug Discovery

October 4, 2022

The drug discovery process can often be long, expensive, and risky. While the pharma drug industry is expected to grow from $53.3 billion in 2021 to $80.2 billion by 2026, it is still dependent on trial and error, leading to delays in new drug approval. These comparatively slower methods have led to the growing adoption of ML/AI at a rate of 30% to support and improve pharma research speeds. This signals a state of change with an emphasis on using ML models over traditional methods. However, there are major ML Ops challenges when it comes to the large and often overwhelming amount of data that those looking to deploy ML models in drug discovery should be aware of. 

Managing High Variable Counts

There are many variables involved in drug discovery, such as the chemical makeup of drugs, their chemical properties, and their interactions with other molecules. This can pose quite the challenge when deploying ML models for drug research as metrics on the data being analyzed are often needed throughout the process. Nearly 90% of clinical trials fail in the first phase due to educated (or uneducated) guesses. If the analysis results are not able to be monitored it can result in billions of dollars lost during the research.

A platform designed with a framework to detect concept data drift by benchmarking distributions can help simplify the identification of preferred chemical combinations by calculating a baseline distribution and measuring how future distributions fall within these results. This level of transparency in your deployment platform allows researchers to tune scoring mechanisms and focus on the various areas that previously slipped through the cracks. Specify the inputs/outputs you want to monitor, set parameters, and define the ideal chemical compounds with more accuracy than ever being notified if the system notices any abnormalities. 

Forward Thinking and Scalable Models

Large-scale clinical data for testing different chemicals are often not fully available during the beginning of drug development. This can often lead to deploying models that have not been designed with the scalability to handle massive data sets in drug research. As there will be this constant flow of new clinical data, the models used in production need the capacity to perform increased computations in a cost and time-saving manner.

Having access to a proper ML deployment solution allows for the development of scalable models that combat this issue. Models that can handle larger amounts of data as it becomes available to allow for a consistent increase in prediction accuracy compared to existing methods. For example, the Bayesian approach allows for modeling complex relationships between large sets of variables. This in turn helps models used in drug development to learn and make sense of these complex behaviors.

Rapid Inference from Large Data Sets

As discussed, one of the biggest challenges in drug discovery is that there are so many chemical combinations that can be used, often resulting in larger data sets than those typically used in ML for different industries. For example, there are over 10^60 (or 1e+60) molecules that can be used in drug discovery. To find the right combination of drugs, millions of combinations of chemicals have to be tested to achieve an effective solution with minimum side effects. However, producing inferences from such large sets of data often takes valuable time that the industry is in short supply of.

However, a purpose-built runtime engine for machine learning, can not only reduce your inference time but provide real-time analysis of clinical trial data at scale allowing researchers more room to identify the perfect combination. Fast and efficient data analysis enhances faster decision-making on whether a drug candidate is worth pursuing. This can save companies Millions in wasted R&D investments and shorten timelines for bringing new drugs to market.

Easier Drug Development and Model Deployment

Wallaroo’s streamlined deployment platform is ideal for getting the most out of your drug development models. Benchmarking has never been easier with our model insights tools that can be configured to immediately detect concept drift. While our high-performance compute engine lets you do more inferencing using fewer resources and our auto-scaling features tailor the resources to variations in load.

Wallaroo can be the catalyst this industry is searching for and allow the development of new drugs faster than ever before. By optimizing the speed of deployment and accuracy of the ML models finding their way into the R&D process, the pharma industry can unlock its true potential. If you want to learn more about the incredible drug discovery solutions offered by Wallaroo, click here to contact our team of specialists.