Most people don’t think about the importance of drug discovery and development until they become patients. It’s the sudden diagnosis of a serious health condition that leaves patients anxiously waiting for scientists to develop a new therapy. People worldwide are also living longer, and often old age brings complex health issues. Because of this, drug discovery and development are more important now than ever. And finding a way to organize the massive amounts of data produced during drug discovery and development is crucial.
The drug discovery market
The drug discovery market is growing. According to Verified Marketing Research, the drug discovery market size was valued at $39.02B in 2020 and is projected to reach $73.17B by 2028, growing at a Compound Annual Growth Rate (CAGR) of 8.2% from 2021 to 2028. The use of artificial intelligence is also growing. The use of AI, specifically related to drug discovery, is projected to grow from $589.5M in 2020 to $2.9B in 2027.
With AI, pharmaceutical companies collect, store, and analyze large data sets at a far quicker rate than by manual processes. Maksym Druchok, PhD, leader of SoftServe’s Life Science R&D team said, “AI now enables a more efficient data-driven approach to drug discovery, development, and repurposing.”
Staggering amounts of data
The challenges in drug discovery and clinical trial workflows are well known—it’s time-consuming, expensive, and inefficient. The biggest challenge of all is the staggering amount of data. Data is vital to every life science company. Dr. Druchok said, “The amount of data generated and analyzed throughout life sciences and omics-related domains has skyrocketed. Today, patient information is collected in real-time and further used to extract observational insights.”
While the industry requires a unique framework to manage data rules and regulations, many pharmaceutical companies don’t have a cohesive, organization-wide approach to managing their data. Even though many are eager to use AI, gaps in understanding still exist.
The R&D team
Because data is collected in real-time with a greater need for data-driven approaches, leaders of pharmaceutical companies need a way to quickly crunch the numbers. SoftServe’s R&D team focuses on the development and application of AI and machine learning methods for more efficient drug discovery and clinical trial workflows. The R&D team has been slowly building an arsenal of tools to tackle the challenges found in drug discovery and development. You can read about those early projects and promising results here. The SoftServe team includes people with backgrounds in academia and industry, a one-two punch that allows the team to attack problems from different angles.
Harnessing the data
SoftServe empowers you to do more than scratch the surface of AI’s potential with solutions for every stage of drug discovery, research, and development. We tie everything together to avoid inefficiencies and gaps, then put building blocks in place to scale it. We focus on the data so you can focus on who matters most—the patient. Projects in the pipeline include drug dosage optimization and cancer immunotherapies.
SoftServe’s solutions advance your research initiatives and empower you to go beyond existing barriers to discover innovative and beneficial solutions—saving time and money.
Specifically, we’ll help you efficiently store and retrieve patient data, health records, and R&D records—allowing more accurate data-driven solutions. You can read about the other benefits here.
If you are searching for a more efficient drug discovery, personalized medicine, and life science platform solutions, let’s talk about how SoftServe technology solutions can accelerate your R&D initiatives.