AI-Driven Medical Breakthrough: Leveraging Artificial Intelligence for Novel Drug Discovery


Drug discovery is known as “from bench to bedside” because of its long duration and high costs. It takes around 11 to 16 years and between $1 billion to $2 billion to bring a drug to market. But now AI is revolutionizing drug development, providing better pace and profitability.

AI in drug development has transformed our approach and strategy towards biomedical research and innovation. It has helped researchers reduce the complexities of a disease pathway and identify biological targets.

Let’s look deeper into what potential AI in drug discovery holds for the future.

Understanding the Role of AI: How It’s Being Used for Drug Discovery?

AI has enhanced different stages of the drug discovery process with its ability to analyze vast amounts of data and make complex predictions. Here’s how:

1. Target identification

Target identification is the first process of drug discovery which involves identifying possible molecular entities like proteins, enzymes, and receptors present in the body that can combine with drugs to produce therapeutic effects against diseases.

AI can leverage large clinical databases that include key information about the target identification. These data sources can include biomedical research, biomolecular information, clinical trial data, protein structures, etc.

Trained AI models along with biomedical techniques like gene expression can understand complex biological diseases and identify the biological targets for the drug candidates. For instance, researchers have developed various AI techniques for the identification of novel anticancer targets.

2. Target Selection

AI in drug discovery can help researchers select promising targets based on their illness correlations and predicted therapeutic utility. With strong pattern recognition, AI can make this selection based not just on declared medical literature but select completely new targets with no prior reference in published patents.

3. Drug Prioritization

In this stage, AI evaluates and rates lead drug compounds, prioritizing them for further assessment and research to advance their development. Compared to previous ranking techniques, AI-based approaches are more effective at identifying the most promising candidates. For instance, researchers have developed a Deep Learning-based computational framework to identify and prioritize novel drugs for Alzheimer’s disease.

4. Compound Screening

AI models can predict compounds’ chemical properties and bioactivity and provide insights into adverse effects. They can analyze data from various sources, including previous studies and databases, to identify any potential risks or side effects associated with a particular compound. For instance, researchers have developed a deep learning tool to screen chemical libraries with billions of molecules to significantly accelerate large-scale compound exploration.

5. De Novo drug design

Manual screening of large collections of compounds has been a traditional practice in drug discovery. With AI, researchers can screen novel compounds with or without prior information and also predict the final 3D structure of the discovered drugs. For instance, AlphaFold, developed by DeepMind, is an AI system that can predict protein structures. It maintains a database of over 200 million protein structure predictions that can accelerate the drug design process.

5 Successful AI-based Drug Discovery Examples

1) Abaucin

Antibiotics kill bacteria. But due to the deficiency of new drugs and the rapid evolution of bacterial resistance against older drugs, bacteria are becoming hard to treat. Abaucin, an AI-developed strong experimental antibiotic, is designed to kill Acinetobacter baumannii, one of the most dangerous superbug bacteria.

Using AI, the researchers first tested thousands of medicines to see how well they work against the bacterium, Acinetobacter baumannii. Then this information was used to train AI to come up with a drug that can efficiently treat it.

2) Target X by Insilico Medicine

Insilico Medicine used its Generative AI platform and created a drug called Target X, now in Phase 1 clinical trials. Target X is designed to treat Idiopathic Pulmonary Fibrosis, a disease that can cause lung stiffness in elderly individuals if left untreated. Phase 1 will involve 80 participants, and half will receive higher doses gradually. This will help evaluate how the drug molecule interacts with the human body.

3) VRG50635 by Verge Genomic

Verge Genomics, an AI drug discovery company, used its AI platform CONVERGE to discover a novel compound, VRG-50635, for the treatment of ALS by analyzing human data points. The data points included information about the brain and spine tissues of patients with neurodegenerative diseases like Parkinson’s, ALS, and Alzheimer’s.

The platform first found PIKfyve enzyme as a possible target for ALS and then suggested VRG50635 as a promising inhibitor of PIKfyve, which became a potential drug candidate for treating ALS. The process took around four years, and now the candidate is in phase 1 of the human trials.

4) Exscientia-A2a Receptor

Exscientia, an AI MedTech company, is responsible for the first AI-designed molecule for immuno-oncology treatment – a form of cancer treatment that uses the body’s immune system to fight cancer cells. Their AI drug has entered the human clinical trials phase. Its potential lies in its ability to target the A2a receptor to promote anti-tumor activity while ensuring fewer side effects on the body and the brain.

Using Generative AI, they have created some other compounds for targeting various diseases like

Transcriptionally Addicted Cancers by targeting CDK7 inhibitors
Inflammatory diseases by targeting PKC-theta enzyme
Hematology and Oncology diseases by targeting LSD1 regulator

5) Absci-de Novo Antibodies With Zero-Shot Generative AI

Absci, a Generative AI drug discovery company, has demonstrated its use of zero-shot generative AI to create de novo antibodies via computer simulation. Zero-shot learning means that the AI model has not been explicitly tested on the current input information during the training phase. Hence, this process can come up with novel antibody designs on its own.

De novo therapeutic antibodies powered by AI cut the time it takes to develop new drug leads from up to six years to just 18 to 24 months, increasing their probability of success in the clinic. The company’s technology can test and validate 3 million AI-generated designs every week. This new development could instantly deliver novel therapeutics to every patient, marking a significant industrial change.

What Does the Future of AI & Drug Discovery Hold?

Besides many other healthcare applications, AI is making the drug discovery process faster and more intelligent by analyzing vast data sets and predicting promising drug targets and candidates. Using generative AI, biotech companies can identify patient response markers and develop personalized treatment plans quickly.

A report suggests that soon, more MedTech companies will incorporate AI and ML into early-stage drug discovery, which will help create a $50 billion market within the next ten years, creating the significant growth potential of AI in pharmaceuticals. AI will potentially reduce overall drug discovery costs, making more novel drugs available to patients faster.

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