Data Visualization System for Drug Discovery Research

In my past articles, I highlight how crucial efficient communication tools are in data-driven drug discovery research. Data generated by experts in various fields, who have dedicated years to their respective domains, needs to be managed, integrated, and analyzed with precision. Hence, a system that can handle these tasks efficiently is vital for drug discovery research.

In my experience, companies, regardless of size, frequently encounter challenges when developing and overseeing data systems. Although I’ll discuss the specific issues in another article, this piece outlines the necessary features for a drug discovery research data visualization system.

1. Data Availability

All data produced and collected for drug discovery research must be easily accessible within the system. Although this may seem straightforward, it actually presents significant challenges. Drug discovery researchers usually obtain data from different laboratory equipment and machines. To obtain the final required formats with appropriate metadata, the data, including manually entered data, must be processed using different analytical software tools.

To make informed decisions in drug discovery research, it’s essential to compare internally generated data with external data obtained from research papers, patents, conference posters, and other sources. Hence, it is important to integrate and analyze both internally generated and externally acquired data using consistent criteria. Adding external data management for comparison increases the complexity of the system’s design and management.

Database solutions typically provide functionalities for searching desired data using different types of queries. In drug discovery project meetings, it would be advantageous for participants to have the ability to easily access and present the required data using LLM-based intuitive natural language search capabilities. It is essential to validate the accuracy of LLM-based natural language search to ensure that decisions are not made using incorrect data.

2. Data Analysis and Visualization

All data needs to be processed using the right analytical techniques, and the outcomes should be visually displayed in diverse manners. Visualizing data always communicates the author’s intent, which is inherently reflected in the parameters used to transform raw data into visible graphs. Therefore, it is sometimes necessary to use different parameters to verify the author’s intent from various perspectives. Some data points may be excluded from the final analysis for various reasons, and these exclusions could significantly impact the final decision.

3. Reports, Dashboards, and Snapshots

Access to decision-making data and visualizations should be available through various forms, such as reports and dashboards, at all times. To account for real-time data updates, decision-making datasets should be saved as snapshots. This allows for a future review of past decisions based on the data available at that time.

Recurring meetings help researchers identify key visualization elements vital for decision-making. Consistently analyzing the same type of visualization enhances analysis efficiency by fostering familiarity with specific visualizations. It is crucial to guarantee accessibility of agreed-upon data visualization formats, like reports or dashboards. Balancing consistent formats with regular introduction of new visualization methods is vital for efficient analysis. Embracing new perspectives through this approach helps avoid the stagnation of thought caused by over-familiarity with particular visualizations.

Decisions made during drug discovery are frequently revisited for various reasons later on. Over time, these reviews enhance the organization’s ability to make better decisions. In order to conduct efficient reviews, it is necessary to save snapshots that contain data and visualization elements from the decision-making period. It facilitates effortless comparison between the original data and newly acquired insights to identify any differences.

Key takeaways:

1. Data Accessibility and Integration: It is essential for drug discovery systems to integrate and manage both internally produced and externally sourced data efficiently. This ensures that comprehensive and accurate information is available for decision-making.

2. Intuitive Data Retrieval: Implementing LLM-based natural language search capabilities can significantly enhance the ease of finding and displaying required data during project meetings, provided that these systems are thoroughly validated to ensure accuracy.

3. Consistent and Evolving Visualization: Consistent use of familiar visualizations increases efficiency in analysis, but it is also important to introduce new visualization methods regularly. Saving snapshots of data and visualizations at the time of decision-making allows for effective reviews and comparisons in the future.