How Large Language Models CanHelp Drug Discovery?

AI can help advance the healthcare and pharmaceutical industry in a much more directed and laser-focused manner. Artificial Intelligence in pharmaceuticals can bring a digital transformation by accelerating drug discovery. LLMs can play a key role in revolutionizing the complex drug discovery process by making it simpler, more informed, and quicker.

Let’s dive into the potential of AI in drug discovery.

Impact of Large Language Models (LLMs) in Drug Discovery

Large Language Models (LLMs) are AI models built and trained on huge amounts of text, images, and data. LLMs' ability to process massive data can enable such AI models to generate information, data, patterns, and much more. LLMs are no longer limited to NLP (Natural Language Processing) applications and can even contribute to specific domains such as drug discovery.

LLMs are:

• Effective in processing vast amounts of information.

• Analyze datasets to find correlations and identify patterns.

• Handy in versatile tasks such as reviewing literature and analyzing clinical trial records.

On the other hand, LLMs are general-purpose models. They are not tailored to a specific industry and as such might struggle with complex relations and interactions. However, there’s a workaround to overcome the limitations of LLMs through a systematic approach utilizing Specialized Language Models (SMLs).

How to leverage Specialized Language Models (SLMs) in Drug Discovery?

Specialized Language Models are tailored to a specific application or domain. These models are fed and trained on very targeted data to gain industry relevance. SLMs offer deeper knowledge and optimized AI applications compared to vast LLMs.

SLMs can help with:

• Domain-specific knowledge systems of AI

• Perform unique industry tasks and applications efficiently

• Learn & address the intricacies and complexities of a domain

Such specific expertise of SLMs can be a great advantage for Drug Discovery applications. However, there is a downside to using SLMs as they are not diverse and must be trained only on a specific type of data in vast amounts.

LLMs + SLMs combined modeling: AI for drug discovery

Drug discovery is not like any other traditional domain leveraging AI. It is much more complex and intricate. Here, successful AI modeling must require diversity as well as specialization. Leveraging the best of LLMs and SLMs can be the most efficient way forward:

• LLMs can help with processing vast amounts of clinical and molecular data to build a knowledge base and extract patterns for AI drug discovery solutions.

• SLMs, on the other hand, are helpful with deeper understanding of chemical structures, down to the molecular level.

• Leveraging both of these models together is the only way for drug discovery applications to manage vast sets of data sets and versatility while also having domain-specific expertise.

Eight ways AI can help expedite drug discovery

Drug discovery is traditionally a complex and tedious process needing massive investment. Drug discovery had a poor drug development success rate of 6.3% in 2022, which shows how drug discovery can be improved for more efficiency. AI can make the process of discovering drugs easy and successful.

Here are some ways AI can help drug discovery:

1. Knowledge extraction in drug discovery:

AI can help with information processing and knowledge extraction into drug discovery. AI LLMs can conduct vast literature reviews to study relevant information about molecules and diseases. AI knowledge extraction can help make drug discovery informed and quick.

2. Building Knowledge Graphs:

Knowledge Graphs are the key to defining relationships among different entities in the domain of drug discovery, including molecules, diseases, and symptoms. Knowledge graphs can help illustrate complex biological systems and correlations.

3. Target identification for drugs:

AI can help identify different molecular targets like proteins and nucleic acids. Target identification in drug discovery helps establish relationships between target proteins within molecules and derive the efficacy of drug discovery.

4. Analyzing Structure-Activity Relationship (SAR):

Understanding the correlation between chemical structure of a molecule and its biological activities is the crux behind drug discovery. LLMs can process the vast amount of drug literature to identify SAR patterns and help design molecules that improve efficacy.

5. Molecular structure generation:

LLMs can also help generate molecular structures that could be useful in creating drugs with specific targets and properties. Generative AI can help save time here. 

6. Drug repurposing for existing drugs

AI into drug discovery can identify drug composition and find new uses for existing drugs that can expedite drug development process and improve efficacy.

7. Polypharmacy analysis

Polypharmacy is the practice of exercising multiple drugs and their effect on one another. Analysis of drug interactions can help improve the efficacy of combination therapies.

8. Effective clinical trials:

AI can help process patient data to identify the most suitable subjects for clinical trials. LLMs can process and recommend ideal populations and demographics to make clinical trials effective.

These are just some of the domain-specific uses of AI in drug discovery. Moreover, AI can also assist with its generic applications for day-to-day activities.

Some companies that already use AI for drug discovery

AI is already tried, tested, and implemented in the process of drug discovery by a set of worldwide companies. These instances of companies using AI prove the potential of technology in expediting drug discovery while making it more efficient. 

1. Antidote’s proprietary tech for clinical trials

Antidote is a company focused on conducting effective clinical trials by matching medical researchers with patients. The company provides a platform where patients can find clinical trials from researchers. With AI, data, and resources at its side, the company can expedite clinical trials for drug discovery.

2. Atomwise’s predictive drug discovery

Atomwise is among the leaders of the drug discovery industry. The company leverages neural networks to analyze 100 million compounds every day to predict the potential molecular structure that has the best possibility and efficacy as a medicine. 

3. Insilico’s drug discovery in 46 days

Insilico is an AI-based drug discovery company that successfully discovered a drug candidate using AI and ML in just 46 days. This headline attracted a lot of attention to the use of AI in the drug discovery domain.

In summary,

AI has immense potential to transform drug discovery to a point where it might help find solutions for unsolved medical conditions, as well as improve the treatments for some diseases. As long as privacy and safety concerns are taken care of, AI with LLMs, SLMs, and neural networks can bring significant changes to human lives.