How Pharmaceutical Industry Is Adopting Artificial Intelligence To Boost Drug Research

by Andrii Buvailo

Disclaimer: All opinions expressed by Contributors are their own and do not represent those of their employers, or
Contributors are fully responsible for assuring they own any required copyright for any content they submit to This website and its owners shall not be liable for neither information and content submitted for publication by Contributors, nor its accuracy.

   269    Comments 0

What is “artificial intelligence”?

Artificial intelligence (AI) is an interdisciplinary science concerned with building “intelligent agents”, the field which takes its origin from the 1950s. Intelligent agents are autonomous systems/programs which mimic human intellect, can “observe” an environment using sensors (or receive data inputs), and perform activity towards achieving specific goals using consequent actuators.

An important component of AI is machine learning (ML), that provides systems the ability to autonomously learn from data and improve outcomes over successive iterations without being explicitly programmed (unlike “if-then” type of computer programs).

A notable family of machine learning models is neural networks, and particularly -- deep neural networks (DNNs), leading to a widely marketed notion of deep learning (DL). Such networks somewhat resemble the human’s brain layout and therefore are believed to be the closest modeling framework to human-type intelligence. However, deep neural nets also have fundamental and technological limitations and require large amounts of data for training, in contrast to more traditional statistical models (RF, k-NNs, SVMs etc). The diversity of existing AI models and strategies leads to a notorious complexity of AI adoption, requiring deep and multifaceted understanding of the nuances of the specific tasks to be solved with AI, and the specifications of various AI models and algorithms.   

While AI is a very broad field, embracing numerous modeling paradigms, it is, however, deep neural nets that has been primarily responsible for the majority of latest and most popular breakthroughs in image/video processing, natural language processing, gaming, various pattern recognition applications, and drug design.

For example, Convolutional Neural Nets (CNNs) were shown to be surprisingly effective at solving image processing tasks, while Recursive Neural Nets with LSTM were behind earlier progress in sequence learning, sequence translation (seq2seq), which also resulted in amazing results in speech to text comprehension and the raise of Siri, Cortana, Google voice assistant, Alexa, etc.

Recently, pharmaceutical and biotech companies and academic institutions have been demonstrating a vivid interest in AI applications for various R&D and operational needs in the area of drug discovery, clinical trials, translational science, biomedical research and pharmacovigilance. This surge of interest is inspired and driven, in part, by profound  advances in neural net architectures (2012 -- Alex Net wins ImageNet competition; 2014 -- Generative Adversarial Network (GAN) architecture is introduced), and by illustrative and widely publicized practical achievements of AI in various fields and industries, including:

  • learning to play complex games (like Chess and Go) at human level and above,
  • recognizing speech and text, synthesizing language 
  • revealing complex behavioral patterns (antimalware systems, Youtube prediction algorithms), 
  • understanding and categorizing objects in images and videos (Facebook face recognition, video surveillance)
  • creating ultra-realistic images and videos (e.g. “deep fakes”), 
  • powering driving cars (e.g. Tesla), 
  • powering robots to perform complex tasks (e.g. Boston Dynamics robots),
  • military applications; the list goes on. 

Another driver is the substantial progress over the past 10-20 years in auxiliary technologies (“AI enabling technologies”), such as computing power, data storage, ML-compatible hardware chips, and public cloud infrastructures and cloud-based services.


Industry at a Glance

The emerging field of pharmaceutical AI is still in its early days, with a limited number of players and non-commoditized AI service offerings. Most AI-driven drug discovery platforms and services are largely custom-based and use case-specific; the “best practice” standards for the AI models and algorithms are not yet clearly defined and are usually specific to a particular vendor or a research group.

The existing pharmaceutical AI community is represented by:

  • 300+ early-stage companies (startups/scaleups) globally, offering a wide array of AI-driven platforms and services -- from classical Software as a Service models, to custom data science services, drug discovery (“Drug candidate-as as service”), and clinical trial support/management resources.

    Year-by-year distribution and cumulative numbers of new AI-driven companies in Drug Discovery segment

Data from "The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D" report.
  • domain-specific software providers (like KNIME, ChemAxon, Dotmatics, MolSoft, and others), mostly focused on cheminformatics/bioinformatics, but also offering machine learning-powered tools.
  • top-tier pharmaceutical and biotech companies developing in-house AI-expertise as part of their R&D strategy. Such players often collaborate with external AI-vendors and AI-driven biotech startups to explore pilot programs in drug discovery/basic biology/clinical trial analytics.
  • top-tier technology companies like Google, Amazon, Tencent, entering pharmaceutical space with their top-notch expertise in cutting-edge AI technologies.
  • contract research organizations (CROs) developing expertise in AI to augment their value offering to pharma/biotech customers
  • academic labs in pharma/biotech space, conducting AI-research and developing specialized frameworks and tools relevant to the industry (usually a cradle for future AI startups/spin-outs)
  • non-domain-specific software providers developing AI as a service packages and models, suitable for application in the pharmaceutical research (“out of the box AI”)
  • Open source machine learning tools and frameworks, widely exploited by the life science professionals in their research projects


The pharmaceutical AI community includes several non-profit organizations, promoting and lobbying the AI adoption by the Life Science professionals, including:


It should be noted, that the amount of venture capital, invested into AI-driven drug discovery companies, has been growing steadily, as well as the industry R&D traction of AI startups -- they are constantly engaged in research collaborations and pilots with largest pharmaceutical corporations:

Year-by-year distribution and cumulative amounts of venture capital invested in AI-driven companies in Drug Discovery segment

Data from "The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D" report.

Major industry collaborations, involving AI-driven startups and big pharma players

Data from "The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D" report.


Artificial intelligence Use Cases

There is a plethora of application areas where AI technologies has been exploited in lab experiments, including the following R&D and operational use cases:

  1. De novo small-molecule design and biologics design;
  2. Structure-activity relationship (SAR) prediction;
  3. Improvement in 3D protein structure simulation;
  4. Biomarker discovery and patient stratification; predicting cancer drug responses;
  5. Drug–drug interactions prediction for the unknown combinations;
  6. Disease modeling, hypothesis generation, target discovery and validation
  7. Improved prediction of adverse drug reactions;
  8. Early stage disease diagnostics;
  9. Analysis of high-content screening results (e.g. cell images) in real time, correlation to other types of data;
  10. Improving drug repurposing programs, reanalyzing previous trials data sets for clinical signals of efficacy and safety for new indications;
  11. Improving clinical trial operations (trial design, improving patient enrollment, patient stratification etc)
  12. Facilitating selection of clinical trial sites to improve quality of trials and lower costs.
  13. Streamlining filing and regulatory compliance
  14. Analyzing post-approval data, pharmacovigilance. 
  15. and so on


Most of the above listed academic use cases are also commercialized to various extents by AI-technology vendors, biotech startups, and larger companies, including contract research organizations. 

Learn more about the companies, investments, deals and research focuses, in the analytical report “The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D”.

On a side note, the team at BiopharmaTrend has launched a catalog of mini-reports, covering companies in a wide range of innovative areas: Microbiome, Gene Therapies, Aging Research, Stem Cells, Quantum Theory, RNA-therapeutics, 3D-bioprinting, Medical Virtual Reality and more. Check it out!

Topics: Industry Trends    Biotech Startups   

Subscribe to Newsletter
Share this:              


There are no comments yet. You can be the first.

Leave a Reply

Your email address will not be published. Required fields are marked *