Artificial intelligence in pharma and biotech is the new digital transition in healthcare. The technology is set to revolutionize every step of drug discovery, from research to production and testing to optimization.
And if your question right now is, “What is the role of generative AI in drug discovery?” Then, this blog is where you will find the answer!!
Today, generative AI services are shifting how pharmaceutical businesses operate, and we have all the reasons to advocate in favor of this shift. And, no, it is not just about efficiency or productivity. This advanced technology has a lot more to offer than just that!
First, we will discuss some key trends, the role of generative AI in drug development, the technologies used, and then the benefits & challenges. Then, we will finish with some real-life examples.
Key Trends Of Generative AI In Drug Discovery
- According to a McKinsey report, generative AI can identify a possible cure for a particular disease by predicting the next part of the molecular structure. This can shorten the testing cycle and make the drug discovery process 2.5X faster.
- The same (McKinsey) report shows that generative AI can increase the possibility of a clinical trial’s success by 10% and reduce costs and time by 20%.
- According to Statista, AI-enabled workflows can save up to 40% of the time required to introduce new molecules for trial and 30% at the preclinical stage.
- ResearchNester’s study shows that AI in the healthcare market (globally) will reach USD 99 billion by 2036, with a growth rate of 35% annually between 2024 and 2036.
- Another Statista report stated that by the end of 2023, the market for generative AI in drug discovery was USD 1.5 billion and would grow ninefold by 2036.
Suggested Read – AI in Healthcare
What Is The Role Of Generative AI In Drug Discovery?
The role of generative AI in drug discovery is significant and spans multiple operations and functionalities. Here are some of the roles taken by generative AI in drug discovery.
Molecule Generation
Using generative AI for drug discovery helps researchers design targeted molecules as cures for specific diseases. The AI model, when trained on how chemical compounds react to gain insights into their composition, improving its feasibility, potency, and safety.
Additionally, using generative AI gives researchers access to a simulated environment to test and observe their chemical compounds. They monitor how the chemical reacts and how to optimize it, which narrows the scope of compound testing.
Virtual Screening
Generative AI creates a virtual space for researchers to see how each compound reacts with their targeted proteins. It helps them identify the drugs that are functioning as intended and eliminate the rest.
Initial selection quickens the following steps as the testing pool shrinks to a few promising ones. This improves the prospect of success while reducing resource utilization. With this valuable information, researchers shift their processes towards more strategic and intentional steps.
Enriching Compound Library
Another role of generative AI in drug discovery is expanding the compound library through an ML algorithm and analysis of the compound’s structure-activity relationship. The AI model then predicts possible modifications to help improve multiple facets of the compound.
Based on AI’s predictions, researchers synthesize and test the compounds from a smaller pool of drug candidates to obtain potential options with desired results. Optimizing and refining existing compounds expands the library while saving time and effort with selective testing.
Biomarker Identification
Biomarkers are biological indicators of specific diseases or health conditions. However, unlike clinical symptoms, they are independent of the patient feeling ill. Patients may or may not experience them.
AI models analyze the genomic and proteomics data to pinpoint underlying diseases and determine whether they require urgent attention. Identifying biomarkers also fosters precise medicines. It allows researchers to modify and develop personalized medications based on their unique biological framework.
Drug Re-Utilization
One way to use generative AI for drug discovery is assistive drug development, and another is to reuse existing ones. Using machine learning algorithms, generative AI predicts possible reactions against the targeted disease. These possible reactions might also turn out to be alternative use cases that weren’t known before.
Additionally, since these drugs are being reused, they are already tested, which eliminates the clinical testing stage in the process. Another plus point is that researchers can further inspect any possible side effects with AI.
Personalized Drug Design
One fruitful contribution of generative AI in drug discovery is that it allows healthcare professionals to design custom medicines. These tailored treatments are designed based on patients’ medical histories, biomarkers, genetic structures, etc.
Since the complete treatment is designed around a particular person, it minimizes side effects and makes the overall treatment more effective.
Novel Drug Molecule Development
The most revolutionary role of generative AI in drug discovery is developing molecules from scratch. AI’s ML algorithms allow it to understand complex datasets and compare all drug compounds’ feasibility, binding affinity, and side effects.
Based on the data, these generative AI models can help researchers improve the molecule structure either through combination or elimination. Generative AI in drug discovery makes the newly generated compounds more accurate and efficient.
Polypharmacological Drug Discovery
Polypharmacological drug discovery is an approach where a single compound is created to target multiple sites. With generative AI’s ability to analyze massive data from numerous sources, creating such a compound is possible.
The simultaneous treatment across multiple problem areas makes the solutions personalized yet versatile. The multi-target approach makes healthcare more precise yet diverse enough to resolve all the intricate necessities.
Drug’s ADME Properties Prediction
ADME stands for the drug’s absorption, distribution, metabolism, and excretion properties. Generative AI studies and predicts how a drug functions when ingested in the body. It forecasts every stage, including how the body will absorb, dispense, digest, and eliminate it.
Researchers get a detailed overview of how different drug components are reacting via virtual simulation. Using the detailed reports, researchers eliminate or alter the challenging components for faster drug absorption and quick actions. This minimizes error, reduces time, speeds up the process, and improves accuracy rates.
Delve into our expertise through our generative AI solution, designed especially for the healthcare sector. Learn more about it here – MediMind AI
Which Technologies Power The Role Of Generative AI In Drug Discovery?
Using generative AI for drug discovery is revolutionary, but so are the technologies that assist in actualizing this process. Here are four primary technologies that power the model’s extraordinary capabilities.
NLP
We cannot discuss generative AI for drug discovery without mentioning this technology, natural language processing (NLP). NLP is a technique that helps the AI model understand a prompt written in natural human language, extract vital information from it, and provide relevant output.
In the case of drug discovery, it gathers insights and results from a multitude of research papers and aids researchers with target identification, drug re-utilization, and simplifying knowledge.
GANs
GAN (Generative Adversarial Networks) is a type of generative AI model. Its architecture includes two components: a generator and a discriminator. The generator creates unique data (in this case, molecular structure), and the discriminator authenticates the result.
The model is trained on how the molecules react in varied combinations, as well as their absorption, efficacy, and other crucial details. Then, it is used to generate a realistic and diverse pool of molecules for researchers to test from and optimize them per requirement.
Reinforcement Learning
Do you know about negative and positive reinforcements in psychology? This is the same concept, but for AI models. The learning is based on the reward-punishment system. AI model interacts with the training datasets and learns through user feedback.
The AI model takes the action and determines whether it is right or wrong based on the user’s positive and negative reactions. This feature of generative AI in drug discovery improves drug design and refines molecular structures for seamless compound design.
Transfer Learning
This is a technique where the particular pre-trained AI model is trained again in the related data. Transfer learning is generally used when limited data is available. It allows the AI tool to leverage both datasets (new and old) to generate more accurate output.
In drug discovery, transfer learning can optimize compound or molecule generation using knowledge transferred from previous drug-target interactions. It relies on prior interactions to generate new interaction points.
Want to know more about generative AI’s architecture? Click here – Generative AI Architecture
What Are The Advantages Of Generative AI In Drug Discovery?
Implementing artificial intelligence in pharma and biotech has many advantages, from discovering new molecules to optimizing the existing ones in a fraction of the time.
In fact, according to a McKinsey report, generative AI will bring an annual value of $60 billion—$110 billion across multiple operational avenues in the pharmaceutical industry. The value of research and discovery alone is estimated at $15 billion to $28 billion.
Some of the main advantages of generative AI in drug discovery are:
Rapid Drug Discovery
Generative AI creates a space for researchers to test different molecule combinations and observe how these elements react with the target. Additionally, the generative AI tools suggest possible (new) combinations for faster optimization, simplifying the entire drug discovery process.
Reduced Expenses
Generative AI’s simulation environment allows researchers to experiment virtually before proceeding to the actual testing. The virtual environment helps researchers determine which combinations yield positive results and eliminate the rest, thus narrowing down the list of actual testing and reducing costs.
Custom Medication
Thanks to generative AI’s smart algorithms and modern technology, researchers can go beyond the physical symptoms and analyze the ailment from its root level, i.e., genes. This helps them design personalized prescriptions based on the genetic framework, medical conditions, and other variables.
Faster Clinical Trials
When clinical trials are done traditionally, about 90% of samples fail the clinical trials. This delays the drug trials and impacts the drug discovery process. With generative AI, the accuracy rate can go from 10% to around 79%, thanks to virtual testing and result predictions.
Targeted Drug Development
Generative AI, with its transfer learning model, allows researchers to tap into lesser-known avenues. It utilizes its existing data to understand the new (limited) information and derive useful insights regarding rare diseases. Generative AI in drug discovery opens up the opportunity to cure even once non-curable diseases.
Challenges Involved In Integrating Generative AI In Drug Discovery
Given that AI is still in its early stages and the technology is continuously evolving, many people are unaware of how to add it to the system well. In such a scenario, many variables may pose a challenge when implementing generative AI for drug discovery.
Some of the commonly witnessed challenges of effectuating generative AI in drug discovery are:
Data Biases
Training data is the foundational structure of its functionality, and this is why it is essential that it is free of any underlying biases. AI models trained on biased or limited data will be prone to faulty judgment, resulting in inaccurate drug effectiveness and predictions.
Misrepresentation or underrepresentation is another form of data bias. Missing a complete set of demographics or having a limited sample size affects the accuracy of the results because of the lack of genomic data.
Ethical Concerns
Technology and ethical issues have been going hand in hand for quite some time now, whether it is privacy matters or lack of transparency. Unethical practices and carelessness in managing the AI solution lead to such concerns becoming harsh realities.
These concerns are especially prominent in healthcare, where sensitive data is at stake. Data leaks, privacy breaches, and errors in decision-making are some of the common challenges that fall under ethical issues.
Regulatory Considerations
The healthcare industry is guarded by many laws and regulations that must be adhered to when integrating any digital solutions, including generative AI. This ensures that all the efforts lead to secure solution development and makes your digitalized healthcare institution more reliable.
These include documenting AI’s functionalities, practical usage, data sources, etc. Given that the healthcare industry is highly information-intensive, the primary stages of integration and data training may become challenging in the case of unorganized datasets.
Excessive Generative AI Dependency
The primary role of generative AI in drug discovery is to aid humans in case of any ailment, but that requires thorough testing. This is to make sure that all the drugs designed are safe for humans to consume and side effects (if any) are known beforehand.
The challenge here is over-relying on generative AI’s predictions. The AI solution is as good as the data it is trained on, and data in this field is limited, so there’s a chance of error. Drawing conclusions only on the basis of AI’s predictions would pose a major roadblock down the road.
How Generative AI In Drug Discovery Has Benefitted In Real Life?
While many are still skeptical about whether or not they should move towards digital integrations, others have already invested in researching the role of generative AI for drug discovery. It is not just a foreign idea anymore; these AI solutions have already started to make a difference in the market.
Here are three real-life examples of generative AI in drug discovery:
#1 A New Antibiotic To Kill Drug-Resistant Bacteria – MIT
MIT researchers discovered a compound class that could kill methicillin-resistant Staphylococcus aureus (MRSA) bacteria, which has been drug-resistant so far, using generative AI’s drug discovery method.
The researchers trained the AI model on their previous testing data of about 39,000 compounds against MRSA, along with their chemical structure. Based on the data, the AI model generated a probable pool of anti-bacterias, which eventually led to the discovery of favorable results.
#2 pan-TEAD Inhibitor To Treat Solid Tumor – Drug Target Review
Insilico‘s R&D team used Chemistry42 (a generative AI-based automated software platform) to develop molecule ISM6331, which inhibits the growth of solid tumors.
The team utilized structure-based drug design using AI to release a huge portfolio of more than 6,000 molecules and three best picks, one of which was ISM6331. This molecule showed favorable results with low doses and fewer side effects, which was another beneficial aspect.
#3 An AI Platform For Biomarker Discovery & Target Identification – Journal Of Chemistry
Insilico designed an AI-powered software called PandaOmics, which uses AI and bioinformatics techniques to identify new therapeutic targets and biomarkers or repurpose them.
They utilized deep learning-based architecture to identify patterns in complex datasets automatically, making the company one of the first to use a DL approach for target discovery. It compares the databases and organizes them according to the specific requirements. This simplifies target identification and accelerates clinical trials.
What Is The Future Of Generative AI In Drug Discovery?
We hope that the blog gave you some useful insights into “What is the role of generative AI in drug discovery?” The present scenario indicates the guaranteed success and lasting impact of generative AI in drug discovery.
Though the road to its integration is complex and full of challenges, those who embrace this change will confirm their success for years to come. If you are wondering where to start and how to navigate the digital route, then our experts are here to help you out.
With a team of adept AI professionals, we at OpenXcell are here to help you make your healthcare institution AI-ready. We ensure that you get quality AI solutions and augment the new era of personalized medications for your patients.