10 rules to integrate data science and drug discovery

A team made up of data scientists created a blueprint for how to harness the potential of data science in pharmaceutical industries and produce more precise results during drug development. These simple rules show how companies can encourage a data-oriented culture, and challenge outdated processes that have been hindering progress for decades. Here are ten simple recommendations to help your company navigate the digital transformation journey.

1. Data science is a key discipline in drug discovery.

Biopharma companies have always focused on attracting people with expertise in biology, medicine, and chemistry. But, technological advances have made it necessary to hire specialists who can change drug discovery and develop drugs by managing large data sets. This includes in silico analyses, which could help propel the industry forward. A large amount of medical and clinical data is requiring a different perspective to help drug development progress. Data scientists are now at the forefront of digital transformation in drug discovery.

Collaboration among technology, drug discovery and biology companies reduces time to discover and deliver new potential medicines.

2. Before data generation, get in touch with data scientists

Clinical development is difficult because of low quality data. Contacting data scientists is often an afterthought for biomedical research. Inability to properly design experiments and realize the full potential of data analysis and generation often leads to limited biomedical insights. Your company can communicate regularly with both computational and experimental scientists as well as data scientists to avoid making mistakes in data generation. It also allows your experts to prepare data from various sources for reuse and integration into a common data store.

3. Set the principles of data generation

Researchers developed a FAIR rule to generate data during drug development and discovery. FAIR stands to make it easy to perform meta-analyses at different stages of drug development. This term identifies the key characteristics of data that can be used to create new hypotheses or advance existing programs. Your organization must be able to access, reuse, interoperate and share data. This will give your teams the foundation they need to quickly analyze existing datasets and discover scientific insights.

4. To analyze and visualize data, use an integrated data storage

Data should be considered an untapped resource. This means that data scientists, clinicians, and experiment scientists must develop resources and tools to enable them to collect accurate results and integrate them into decision-making processes. A search engine that allows you to identify entities and their relationships is essential for an ideal model. This is a list of possible features that the user could find in the database

  • Targets
  • Compounds
  • Indications
  • Biological pathways
  • Experiments
  • Studies
  • Portfolio projects

An integrated data store also requires that your organization create application programming interfaces, (APIs), to collect results and interactive graphic user interfaces (GUIs), to visualize them in an easy-to-access form.

5. Collaboration between data scientists from different locations

Biopharma companies once had centralized data science teams that supported other departments. This traditional way of communicating with data scientists has become less effective as they are unable to access project details. The greatest innovation in this instance was the hiring of computational specialists to work within a company’s departments. This distributed model allows data scientists access to new knowledge in specific research areas and creates a network of specialists who can share their insights and collaborate across the company.

6. Technology-oriented culture should be nurtured

Without a solid understanding of experimental and biological data, experts in data science will not be able to produce the desired results. This applies to all specialists in drug discovery, with the increasing demand for digital proficiency in biopharma firms. Employees can learn data science basics and discover new ways to analyze external and internal datasets to achieve specific goals in drug discovery. Technology-driven organizations foster collaboration among specialists.

7. Recognize the limitations of AI/big data technologies

AI offers distinct advantages in many areas of drug discovery. These include improved disease understanding and more efficient clinical trial designs. However, Machine Learning has been around for decades, even though it is now very popular. AI and big data in pharmaceutical industries still have many limitations. The analysis of large, annotated datasets is inevitably a complex challenge. A false picture of AI’s strengths or weaknesses can lead to unrealistic expectations about miraculous transformations. It is possible to expect miraculous transformations if you have a wrong picture of AI’s strengths or weaknesses. New technologies should not be viewed as a panacea. Instead, they should be a powerful tool for realizing the potential of data science in pharmaceutical.

8. Promote strategic partnerships

For strong partnerships with pharmaceutical companies and academic institutions, it is important to contextualize internal data with public data. Your organization can provide access to relevant data, publish relevant data, and help promote data solutions. The future of the industry can be shaped by collaborative and precompetitive projects. AI is a tool for collaboration, according to major biopharma companies.

Statistics show that AI was integrated into the discovery process of major biopharma companies by using data scientists and AI experts, as well as creating new opportunities for collaboration.

9. Give data science teams enough resources

There are no two drug discovery projects the same. Therefore, data science implementations for different purposes will require specialized expertise. Data scientists can be responsible for curation, analysis, integration, analysis, and/or data mining. A company should provide its employees with the data, software licenses and other collaborations they need to accomplish their objectives.

Find out how created an innovative drug ordering system that automatically communicates the drug order to a pharmacy and then displays messages related to predetermined situations of the user.

10. Attract talent and support your experts

Biopharma continues to be transformed by the increasing demand for data scientists. For improved decision-making, forward-looking companies look for people who can combine computational modelling with domain knowledge. It is important to give data scientists complete access to project details and research areas as companies create new drug discovery teams.

Closing remarks

Companies can use data science to accelerate drug discovery and quickly extract valuable insights from data. Biopharma companies must be on the forefront of innovation and view strategic partnerships as a key tool for success. These rules should help you maximize data value and provide the best treatment options for your patients.

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