Learn More About Drug Discovery Software
What is Drug Discovery Software?
Over time, the pharmaceutical and biotechnology world has leveraged electronic data capture (EDC) and laboratory information management solutions to support their research and development efforts. It only makes sense that technology has evolved to provide the same type of process automation, data management, and other kinds of assistance to the drug development lifecycle.
Drug discovery software ensures that all calculations, determinations, and trials are thoroughly completed , without human error, and without potential bias. Drug discovery software also helps researchers, chemists, and scientists scale their efforts via process standardization, storage and duplication of data, accurate candidate identification, and alignment with risk and compliance measures. Drug discovery software significantly cuts down on the time, energy, and resources previously spent in developing new drugs. Drug discovery software makes it possible for scientists to positively impact the management of rising chronic diseases.
Key Benefits of Drug Discovery Software
- Ensures reproducibility of results
- Cuts down on image analysis times
- Increases the quality, relevance, and impact of aggregated data
- Helps pharmaceutical companies to remain competitive in the drug market
Why Use Drug Discovery Software?
The drug development process has historically been complex, expensive, and time-consuming. That’s not including all the quality management and regulatory aspects that newly developed drugs have to undergo to get to market. Drug discovery software leverages existing technology, for both the benefit of pharmaceutical companies that can make a profit off new drugs and for patients who can now have access to drugs that had not previously existed.
R&D productivity is significantly improved with drug discovery software. Automation is crucial to speeding up the drug development process but it also reduces the margin of human error via machine learning, simulations, and data-mining technology. Additionally, drug discovery software stops researchers from leaning so heavily on chemistry alone. Researchers can now take advantage of all the existing drug, assay, molecular, and protein information out there.
Time saved — Technologies and methods like machine learning services and artificial intelligence help scientists parse through massive data sets, which enables the rapid development and launch of new drugs to market. Instead of relying on chemistry alone to approve or deny drug development, scientists and labs can use computers and other computational methods to analyze and generate insight about the drug in development.
Automation — Tasks like high-content screening (HCS)—which automates the process to identify the kinds of target cells and ways substances can alter them—and high-throughput screening (HTS)—which sorts through existing compounds to narrow down the number of new drug candidates—used to be incredibly labor-intensive. The time that scientists had to spend waiting for the systems to search through existing drugs to find potential candidates used to be weeks, if not months. With automation, that time is reduced to mere hours. That means that R&D teams can focus more on tweaking and adapting drugs instead of waiting in limbo for approval.
Who Uses Drug Discovery Software?
Drug discovery software fulfills one particular need: automate (and thereby streamline and speed up) the drug development lifecycle. Accordingly, there is a very specific user demographic of drug discovery software:
R&D scientists — Researchers and scientists who work in the pharmaceutical industry and biotechnology laboratories rely on drug discovery software to become more productive, automate time-consuming tasks, and keep track of the work they have done so far in the lab.
Clinical trial organizers, managers, analysts — The near-final step of drug discovery is running it through clinical trials. While clinical trial organizers can rely on CTMS to take care of actual trial intricacies, they can depend on drug discovery software to reduce the time spent on screening for drug candidates.
Drug Discovery Software Features
Drug discovery solutions are constantly coming up with new and improved features, but the following features are fairly typical and standard across the board:
Prediction — Automated, predictive formulaic calculations that generate data, identify potential targets, determine interaction and activity predictions, and identify potential defects of developing drugs help speed up the drug discovery process.
Virtual screening — Scans and searches through libraries of chemical compounds and molecular structures against drug targets. Some virtual screening modules can be configured to select particular compounds. Virtual screening accelerates the drug discovery process by significantly reducing the potential cost to analyze, detect, and analyze the developing drug’s molecular dynamics and protein ligand structural components.
Docking — Predicts the binding affinity between two molecules, which is used during the drug design process. Additionally, regular molecular docking assessments are required for the product’s docking functionality to be as effective and accurate as possible.
Workflow management — Efficient and comprehensive workflow tools can speed up pharmaceutical process development. Workflow management features can include better data and information exchange, process standardization, and automation of IT processes.
Potential Issues with Drug Discovery Software
Scale — Many of the technological innovations and discoveries that have been presented and discussed as positively impacting the drug discovery industry haven’t yet been implemented on a large scale. This poses a few problems, including unknown regulation complications and duplication problems.
Data management — Researchers and scientists may become overwhelmed by the sheer amount of data that can be generated once drug discovery processes are automated. While more data equals more context and use cases to refer to, more data also requires effective data management and analysis solutions for scientists to leverage the data.
