Modern drug discovery research uses multiple powerful technologies to provide data on many aspects of a candidate molecule, such as molecular activity, cellular processing, genomic sequencing, toxicity, and more. Concurrently, modern high-throughput workflows can generate enormous datasets. The abundance of data made possible by these modern capabilities requires a powerful data management platform to process and store that data. Informatics has been advancing alongside research technologies to do just that.

Informatics was described in the 1990s as the science, engineering, and technology of computer hardware, software, and communications. The advances of the last 30 years have made informatics an even more powerful tool for collecting, organizing, storing, analyzing, and interpreting large quantities of data. This is due in large part to the integration of sophisticated artificial intelligence (AI) tools that are particularly useful for managing large and complex datasets such as those generated in drug discovery research.

Informatics in drug discovery

Informatics plays an important role in multiple aspects of drug discovery and development. Using machine learning AI, today’s advanced informatics are being used to:

• Streamline and help automate workflows
• Enhance the identification and characterization of potential drug targets
• Help discover new chemical entities and for structure-based drug discovery
• Speed up the screening and optimization of lead compounds
• Facilitate the prediction of drug-target interactions
• Assist in the identification of drug metabolism and pharmacokinetic properties
• Enable the analysis of adverse drug reactions and drug-drug interactions

In terms of data management, informatics systematically organizes and stores data such that it can be easily retrieved and used for further analysis, to inform additional research plans, and to collaborate with other researchers. It can also be used to document and save research methods, allowing for reproducibility and the associated confirmation of the reliability and validity of the research findings.

Challenges in the application of informatics

As with any advanced technology, challenges exist in optimizing the use of informatics. Using informatics for biological applications such as drug discovery research can present unique challenges.

First and foremost is the need for high-quality data. The best first step in meeting this need is to develop and integrate a rigorous quality control program for all workflows. Even so, biological data can be inherently rife with noise and errors. Efficient algorithms are needed to screen, clean, and standardize such data.

Standardized data formats and ontologies are needed to facilitate data integration so that all layers of data are analyzed together to provide a comprehensive evaluation of all available datasets. Standardized data formats also help optimize the interoperability of the many tools, workflows, and types of data encountered in drug discovery research.

Overcoming these and other challenges in “bioinformatics” requires a multidisciplinary approach that combines expertise in biology, computer science, AI, and others.

Considerations for selecting an informatics platform

Choosing an informatics platform for your research is not for the faint of heart. Here are a few key aspects to remember as you consider different options.

Customization: You need a platform that can be customized to meet your specific research needs. A one-size-fits-all model will outlive its usefulness sooner rather than later.

Data management: Confirm that the platform can handle the type of data you are working with and that it provides the tools you need for managing and analyzing your data. Be self-centered about this.

Scalability and integration: Choose a platform that can be scaled to accommodate increasing data volumes and complexity, and can integrate with other systems or tools you are using or expect to use in the future.

Security: Insist on a platform with robust security measures to protect your data and meet any internal or regulatory requirements.

Dr Christian Schüller, Field applications scientist at Revvity Signals™ Research Suite, spoke how the Signals platform enables researchers to leverage informatics to accelerate drug discovery, enhance efficiency, and uncover novel therapeutic compounds.