Drug discovery is referred to as “from bench to bedside” due to 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 higher 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 discover biological targets.

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

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 research vast amounts of information and make complex predictions. Here’s how:

1. Goal identification

Goal identification is the primary strategy of drug discovery which involves identifying possible molecular entities like proteins, enzymes, and receptors present within the body that may mix with drugs to supply therapeutic effects against diseases.

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

Trained AI models together with biomedical techniques like gene expression can understand complex biological diseases and discover the biological targets for the drug candidates. As an illustration, researchers have developed various AI techniques for the identification of novel anticancer targets.

2. Goal Selection

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

3. Drug Prioritization

On this stage, AI evaluates and rates lead drug compounds, prioritizing them for further assessment and research to advance their development. In comparison with previous rating techniques, AI-based approaches are more practical at identifying probably the most promising candidates. As an illustration, researchers have developed a Deep Learning-based computational framework to discover and prioritize novel drugs for Alzheimer’s disease.

4. Compound Screening

AI models can predict compounds’ chemical properties and bioactivity and supply insights into hostile effects. They’ll analyze data from various sources, including previous studies and databases, to discover any potential risks or negative effects related to a selected compound. As an illustration, researchers have developed a deep learning tool to screen chemical libraries with billions of molecules to significantly speed up large-scale compound exploration.

5. De Novo drug design

Manual screening of huge collections of compounds has been a conventional practice in drug discovery. With AI, researchers can screen novel compounds with or without prior information and in addition predict the ultimate 3D structure of the discovered drugs. As an illustration, AlphaFold, developed by DeepMind, is an AI system that may predict protein structures. It maintains a database of over 200 million protein structure predictions that may speed up the drug design process.

5 Successful AI-based Drug Discovery Examples

1) Abaucin

Antibiotics kill bacteria. But because of the deficiency of latest drugs and the rapid evolution of bacterial resistance against older drugs, bacteria have gotten hard to treat. Abaucin, an AI-developed strong experimental antibiotic, is designed to kill Acinetobacter baumannii, probably the most dangerous superbug bacteria.

Using AI, the researchers first tested 1000’s of medicines to see how well they work against the bacterium, Acinetobacter baumannii. Then this information was used to coach AI to provide you with a drug that may efficiently treat it.

2) Goal X by Insilico Medicine

Insilico Medicine used its Generative AI platform and created a drug called Goal X, now in Phase 1 clinical trials. Goal X is designed to treat Idiopathic Pulmonary Fibrosis, a disease that could cause lung stiffness in elderly individuals if left untreated. Phase 1 will involve 80 participants, and half will receive higher doses step by step. This may 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 find a novel compound, VRG-50635, for the treatment of ALS by analyzing human data points. The info points included information concerning 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 goal for ALS after which suggested VRG50635 as a promising inhibitor of PIKfyve, which became a possible drug candidate for treating ALS. The method took around 4 years, and now the candidate is in phase 1 of the human trials.

4) Exscientia-A2a Receptor

Exscientia, an AI MedTech company, is liable for the primary AI-designed molecule for immuno-oncology treatment – a type 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 focus on the A2a receptor to advertise anti-tumor activity while ensuring fewer negative effects on the body and the brain.

Using Generative AI, they’ve created another compounds for targeting various diseases like

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 signifies that the AI model has not been explicitly tested on the present input information throughout the training phase. Hence, this process can provide you with novel antibody designs by itself.

De novo therapeutic antibodies powered by AI cut the time it takes to develop recent drug leads from as much as six years to simply 18 to 24 months, increasing their probability of success within the clinic. The corporate’s technology can test and validate 3 million AI-generated designs every week. This recent development could immediately deliver novel therapeutics to each patient, marking a major industrial change.

What Does the Way forward for 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 corporations can discover patient response markers and develop personalized treatment plans quickly.

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

If you desire to know more about AI and the way it’ll shape our future, visit unite.ai.