Artificial Intelligence is not a new concept in drug discovery, but it has gained momentum in the last decade thanks to the availability of large and diverse data sets, advances in computational power and algorithms, and increased investment and collaboration.
Advancing Research Potential in Pharma
AI is becoming more popular in the pharmaceutical industry as drug development costs increase. By integrating AI with advanced biology and chemistry methods, new algorithms can enable virtual drug screening with minimal experimental or human input. Many large pharmaceutical companies have launched internal initiatives or formed collaborations to use AI platforms for drug discovery, aiming to boost the research potential of drugs for immuno-oncology, metabolic disorders, cancer, and other therapeutic areas.
AI in drug discovery offers the promise of:
- reducing timelines and costs
- improving the quality and novelty of molecules discovered
- increasing the probability of success of R&D programs.
The research by McKinsey has identified nearly 270 companies working in the AI-driven drug discovery industry. According to a recent analysis by Boston Consulting Group, “biotech companies using an AI-first approach have more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials”. But how exactly is AI being used to accomplish these milestones, and why does it matter?
AI Applications in Pharmaceutical Research and Development
The conventional approach to drug discovery is a lengthy and costly process, with the stages before clinical trials usually lasting between three to six years and requiring hundreds of millions to billions of dollars. However, a variety of AI tools are transforming almost every step of the drug discovery process, providing significant opportunities to improve the efficiency and affordability of the industry. Here are some of them:
Target Discovery and Validation
At this stage of drug discovery, AI is learning from various sources of data, such as including omics datasets, phenotypic and expression data, disease associations, patents, publications, clinical trials, research grants, and others, to reveal the biological pathways of diseases and to discover novel proteins and/or genes that can be targeted to treat those diseases.
Lead Compound Identification
AI algorithms can efficiently sift through massive chemical databases and predict the potential activity and key properties such as toxicity, bioactivity, and the physicochemical characteristics of small molecules, aiding in the identification of promising lead compounds for further development.
Candidate Drug Compound Prioritization
AI is applied to evaluate and order a group of potential “lead” drug molecules that have been discovered for their further assessment, showing better performance than previous methods of ranking.
Drug Design and Optimization
By allowing high-accuracy molecular simulations that can be performed entirely on computers (i.e., in silico) without the expensive drawbacks of conventional chemistry methods, AI is also helping to decrease the necessity of physical testing of candidate drug compounds.
Clinical trial matching
AI algorithms are used to analyze various datasets, such as electronic health records, patient demographics, previous clinical trial results, and omics data, to match patients to suitable clinical trials, potentially making those trials more statistically powerful.
Key Factors Driving the Rapid Growth of AI in Drug Discovery
AI in the drug discovery market is experiencing rapid growth and is expected to reach a value of $4.0 billion by 2027, with a remarkable CAGR of 45.7% from 2023 to 2030. This growth can be attributed to several key factors that are driving the adoption of AI in this field.
One significant factor is the abundance of big data and its accessibility. The proliferation of data from genomics, proteomics, and other sources provides a vast resource for AI algorithms to analyze and extract valuable insights.
Advancements in computational power have also played a vital role in fostering the growth of AI in drug discovery. Increasing computational capabilities, such as cloud computing and high-performance computing, allow for the efficient processing of complex algorithms, enabling more sophisticated AI-driven drug discovery methods.
Collaboration and partnerships have also contributed to the expansion of AI into the field of drug discovery. By joining forces, technology companies, pharmaceutical giants, and academic institutions create an environment of knowledge exchange and resource pooling.
As the advantages of artificial intelligence become more apparent, stakeholders in the healthcare industry are increasingly recognizing its potential in revolutionizing drug discovery processes. This growing awareness and acceptance directly contribute to the market’s expansion.
Regulatory support is equally significant in driving the growth of AI in the drug discovery market. Recognizing the potential of AI, regulatory bodies are actively working towards establishing guidelines and frameworks to encourage its responsible integration into the industry.
One example of this is the FDA’s proposal to approach Artificial Intelligence in Drug Manufacturing from a regulatory standpoint. This initiative aims to foster alignment with the industry, and then define a balanced regulatory framework that enables innovation while ensuring a shared understanding of quality.
Similarly, in the UK, a collaborative partnership was formed between the Alan Turing Institute, the British Standards Institution (BSI), and the National Physical Laboratory (NPL). Together they are working towards shaping global technical standards for AI, aiming to reduce bias in data used by AI technology. This action was part of the UK’s National AI Strategy.
Through these regulatory actions, the industry is taking proactive steps to create a conducive environment for the integration of AI in drug discovery.
Expert Support You Need
Companies that successfully leverage AI to accelerate their processes are those that possess certain capabilities. These capabilities include the ability to connect and make sense of diverse and imperfect datasets from various sources, as well as different therapeutic approaches. They also require a well-designed technological infrastructure that enables quick adoption of AI advancements, and they can smoothly integrate computer-based systems with their scientific and operational workflows.If your company wants to achieve AI-enabled acceleration, you may need some help from experts in AI-driven systems. With their high expertise in developing and deploying AI solutions, Softarex experts are here to help you. Contact us today and see how you can speed up your research and development in the biomedical field with AI.
