Data visualization has a long history. During the Crimean War in the 19th century, Florence Nightingale worked with a medical statistician to use the data to create a map of polar areas showing that far more soldiers died from preventable diseases than from battle injuries. Beginning in the 1850s, Nightingale’s use of statistics and visual charts helped transform health policy and practice in military hospitals.
Today, data visualization plays a vital role in many fields, from agriculture to financial management. In drug discovery, data visualization can help reveal hidden patterns and trends. It can also support collaboration, increase transparency and create a narrative around scientific information, thereby communicating important facts to a wider audience, including the general public.
Here are some benefits of data visualization in drug discovery, including small molecule design:
Identify patterns and trends
Data visualization can reveal data patterns that are hidden using traditional methods. Using machine learning (ML) and other techniques, early drug discovery teams can use data visualization to discover and optimize lead compounds. Computer-aided drug design (CADD) and computer simulation processes would not be possible without data visualization tools.
Since the mid-2000s, various biopharmaceutical companies and government organizations have used data mining and data visualization to discover adverse drug reactions (ADRs). Data visualization facilitates computer modeling of potential ADRs.
In the past, this type of data monitoring has uncovered problems with the weight-loss drug fenfluramine (fenphen), leading to its withdrawal from the market. In the public sector, data mining of public forums and social media can uncover early instances of ADR. The power of contemporary data mining and visualization can speed up this process and save lives.
Knowledge graphs can help teams working in different teams in different parts of the world visualize the connections between their work. For example, the Clinical Knowledge Graph (CKG) is an open-source platform that illustrates ~220 million relationships covering relevant experimental data.
CKG uses flexible data models combined with statistical and machine learning algorithms to create data visualizations that support broad international collaborations in the biosciences.
Sophisticated data visualization and collaboration tools can create a Trusted Collaboration Environment (TCE) that supports fundamental concepts of transparency, data security, and audit trails. TCE provides protection for proprietary algorithms and proprietary data and processes, while enabling collaboration and advancement of individual teams, as well as the benefits of collaborative and collaborative research and investigation.
Increase trust and transparency
Have you heard the saying “a picture is worth a thousand words”? Drug discovery is a highly technical process, filled with numbers and jargon that the general public is unlikely to understand. Data visualization helps convey important concepts to the public in an easy-to-understand, transparent manner.
In the UK, the Association of the British Pharmaceutical Industry (ABPI) has formally launched an initiative to use data visualization to show the public how pharmaceutical companies are committed to being transparent and responsive to the public’s needs.
Helping Researchers Tell Their Stories
Companies can use the power of data visualization to tell the story of their drug discovery process. Peter Chobanian, director of marketing analytics at Ogilvy Health, told PharmaVoice that he uses data visualization to create compelling data stories that keep people engaged. Data visualization can help your business identify and present the data about your drug discovery process that will most resonate with the public, investors or business partners.
How to Use Data Visualization in Research
Today’s fast-paced scientific research environment is a far cry from the early paper-and-pencil methods of 19th-century healthcare pioneers such as Florence Nightingale. Today’s biopharmaceutical professional processes millions of data points.
It looks like data visualization in the drug discovery space is here to stay, the challenges we face are increasing, and we are dealing with more data than ever before. Still, technology continues to evolve and improve, helping not only scientists and researchers, but all stakeholders in the broader drug discovery ecosystem to visualize data like never before.