Best Drug Discovery Software

What is Drug Discovery Software?

Why Use Drug Discovery Software?

Who Uses Drug Discovery Software?

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.

Trends Related to Drug Discovery Software

The drug discovery market rises and falls based on levels of chronic diseases, demand for drugs, and advancements in the field of biotechnology. Additionally, expiration of patents, inevitable pathogen resistance, fierce competition between drug manufacturers, and the evolving ability of technology to treat previously untreatable diseases, all lead to the growth of drug discovery solutions.

Precision medicine — Data powers precision medicine tactics, with the ultimate goal of leveraging a more holistic view of the individual patient. Precision medicine can bring about bettered targeted therapies because it does away with generalized demographics. Better personalized medicine and therapies also provide scientists with more data that they can use to refine their R&D efforts and fine-tune candidates for any clinical trials.

Artificial intelligence — Artificial intelligence (AI) accelerates the discovery and development of new drugs. AI has the power to improve drug affinity predictions, which closes the gap between drug development and inconclusive clinical trial results. The application of AI to the drug discovery process also has the potential to drastically reduce the cost of development.

Deep learning — Deep learning technology goes one step further than AI to be able to more effectively process all the information needed to conduct scientific research. Scientists can leverage deep learning to scan through and analyze the thousands of publications and drug databases that must be parsed during the drug development stage, especially during targeting.

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.

Software and Services Related to Drug Discovery Software

Clinical trial management — In order for drugs to get to market, they must undergo and pass clinical trials so that they obtain approval from federal governmental bodies. Clinical trial management software is used by pharmaceutical companies to QA the drugs, track the results of the clinical trials, and manage trial participants.

Quality management — Health care-specific quality management software (QMS) helps pharmaceutical companies and regulatory bodies inspect, document, and report on newly developed drugs. By the time new drugs make it to the quality management stage, they’ve usually passed the clinical trial stage, so separate software is required to make sure nothing falls through the gaps before drugs are brought to market.

CRM in pharma and biotech — Between clinical trial volunteers and participants to manage, clinical researchers and experts to consult, and pharmaceutical reps to create relationships with, some sort of contact or chemical database needs to be created, maintained, and consulted. This is where CRM in pharma and biotech software comes into play.

Disease management — Disease management software can be used as a reference point for any pharmaceutical company or drug developer before the drug discovery process gets underway. Disease management software maintains and monitors ongoing health conditions via data pulled from aggregate sources, an invaluable resource for those who want to bring an entirely new drug to market and beat out the competition.