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Artificial Intelligence in Drug Discovery Industry Analytical Framework

Deep Knowledge Group (DKG) and its analytical associates have constructed intricate analytical frameworks competent enough to analyze, define, and predict the Artificial Intelligence in Drug Development (AI in DD) industry and the DeepTech technologies that drive it. For the past decade, Deep Knowledge Group has been developing the most practical means of advancing, optimizing, predicting, and coordinating the trajectory of Artificial Intelligence in Drug Development’s constant advancement and the careful, de-risked, and socially responsible delivery of its benefits for humanity. 

To this end, Deep Knowledge Group has developed the Artificial Intelligence in Drug Development Industry Analytical Framework as a thorough and comprehensive framework for sector and industry analysis that makes it easier to compare businesses internationally and focuses on the technological aspect of each company's business activity.

AI in Drug Discovery Industry Analytical Framework is developed to provide the comprehensive overview of components of AI in the Drug Discovery Industry. 

AI in the Drug Development Industry Analytical Framework consists of three main segments: Focus on Applications of AI for Drug Discovery, Focus on Application of AI for Oncology Diagnostics and Treatment, and Established Drug Discovery-Oriented Entities.

The last segment contains five larger subsegments, namely, Early Drug Development, Clinical Drug Development, Preclinical Development and Automation,  End-to-End Drug Development, and Data Processing.

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Full Set of Framework Documentation

Framework Summary
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Framework Deck
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Full Framework Document
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Artificial Intelligence in Drug Discovery Analytical Framework

Advanced R&D

Biomarkers Development

Drug Discovery

AI-Assisted Diagnostics

At-Home Cancer Detection

Clinical Decision Support

Medical Images Analysis

Patients Outcome Prediction

Personalized Treatment Options 

Established Drug Discovery-Oriented Entities

Compounds Classification



Identifying New Drug Candidates

Identifying New Drug Pathways

Identifying New Drug Structures

Identifying Drug to Drug Interactions

Identifying New Drug Indications

Identifying New Metabolic Pathways

Identifying Suitable Patients

Hit Identification

Lead Optimization

Predictive Drug Modeling

Target Identification


Imaging Analysis

Patient Stratification

Predictive Modeling

Real-Time Monitoring

Automated End-to-End 
Drug Analysis

Automated End-to-End Production

ADME/PK Modeling

Experiment Data Analyzing

Preclinical Protocol Optimization

Robotic Hands

 High Throughput Screening

Chemical Data  Analyzing

Clinical Trials Data Analyzing

Predictive Patient Reaction Modeling

Virtual Experiment Processing

Drug Safety Improving

Preclinical Trials Prediction

Preclinical Imaging Analysis





Data Analysis

Lab Experiments Data Analyzing

Early Drug Development

Early drug development is the stage of drug development that occurs before preclinical and clinical development. It involves identifying potential drug candidates, conducting initial testing to determine their pharmacological properties, and selecting candidates for further development. This stage has several peculiarities that distinguish it from other stages of drug development.

Clinical Drug Development

Clinical drug development is the stage of drug development that involves testing the safety and efficacy of a drug candidate in humans. This stage is typically divided into three phases, each with its peculiarities.

Data Processing

Data processing is an essential step in drug development as it involves analyzing and interpreting data to identify potential drug candidates and understand their safety and efficacy. 

Preclinical Development and Automation

AI has been increasingly used to support preclinical drug development by modeling the properties and potential outcomes of drug candidates. One way AI can do this is by analyzing the properties of a drug candidate's structure, such as its molecular weight, size, and shape, to predict its activity and efficacy. AI can also analyze genetic variations in specific cellular lines or mice strains to simulate preclinical studies and make predictions about potential efficacy and toxicity.

Focus on Applications of AI for Drug Discovery 

The field of using Artificial Intelligence for drug discovery is a rapidly growing area of research that has the potential to revolutionize the process of drug discovery and development. 

Focus on Applications of AI for

Oncology Diagnostics and Treatment

There is a growing interest in the applications of AI for oncology diagnostics and treatment as the use of AI has the potential to greatly improve cancer care. AI algorithms can analyze large amounts of patient data, medical images, and treatment history to identify patterns and features that are associated with treatment response and toxicity, and use this information to develop personalized treatment plans for individual patients.

End-to-End Drug Development

End-to-end drug development is a comprehensive approach to drug development that involves all stages, from discovery to commercialization. The process can be divided into several stages, each of which has its peculiarities

The Artificial Intelligence in Drug Development Industry Analytical Framework comprises two main components:


  • An Industry Classification Framework (ICF) for assigning industry entities to sectors and subsectors without which no further analysis is possible.

  • A SWOT analysis methodology and set of associated parameters, a framework for identifying and analyzing an organization's strengths, weaknesses, opportunities, and threats.


How are these frameworks and parameters devised? As the first step, DKG’s analytical subsidiary Deep Pharma Intelligence (DPI) collects data from multiple sources. Then, the sources are consulted on various scientific and financial information about companies: companies’ selection, investors, financial rounds, IPO status, total funding amount, patents information, scientific publications, news, collaborations information, etc. Source data is then subject to data accuracy review, and a data aggregation pipeline process includes the search for approximately 170 different parameters, from the number and type of patents and H-index of the companies' representatives to website visiting dynamics, financial indicators, and much more. After the data aggregation, we clean and transform the data, so that they can be used for the SWOT creation.

Access Deep Pharma Intelligence Big Data Analytics Dashboard

AI for Drug Discovery Industry Analytical Framework formed the basis of the Deep Pharma Intelligence Big Data Analytics Dashboard, tracking the status of 700 companies, 1,400 investors, 260 collaborations, various clinical trials, publications, news, and a large amount of other valuable information. This delivers advanced market intelligence, interactive mindmaps, benchmarking for companies, investors, and technologies, competitive and SWOT analysis.

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