The use of drugs can be dated back to the 4th century B.C. when opioids were used for medicinal, religious and recreational purposes. Since then, drug discovery has become much more advanced with the incorporation of constantly emerging technology. The most recent advancement in the drug discovery process involves the use of artificial intelligence and machine learning algorithms. The AI R&D market increased from US$200 million in 2016 to more than US$700 million in 2018. Researchers expect it to reach US$20 billion in the next five years.
Drug discovery is a crucial aspect of the health industry. The main goal behind it is to bring forth a new compound that is proven to have a therapeutic effect to its users. Although our understanding of biological systems has progressed to a large extent, drug discovery is still a very long, inefficient and capital-intensive process.
Let’s have a walk through into our findings on how the use of AI in drug discovery and development helps us overcome roadblocks faced by researchers in healthcare.
The drug discovery process
Discovering a drug starts with target identification and validation. A drug target refers to a molecule in the body that can interact with a potential drug compound to produce a clinical effect. After identifying a target, researchers will have to narrow a variety of compounds to one particular compound that could potentially become a drug.
As it is impossible to predict which chemical structures will have both the desired biological effects and the properties needed to become a viable drug, the process of developing a compound into a potential drug is extremely expensive and time-consuming. The average cost for research and development of a successful drug is estimated to be $2.6 billion. The overall probability of clinical success is estimated to be less than 12%.
The importance of AI in drug discovery
As you all know, the proper use AI in healthcare helps to considerably reduce diagnostic and therapeutic errors, deliver real-time health alerts and could even predict possible health outcomes of a patient from time to time.
But that’s not all,
The right use of artificial intelligence technology can even replace human researchers.
Let’s have a look at the advantages that artificial intelligence brings us in drug discovery and development process.
Cost: Although the initial investment in artificial intelligence may be high, companies can save up a lot of money on hiring skilled human resources for various aspects in the drug discovery process.
Speed: Humans take a very long time to narrow down chemical compounds that will have the desired therapeutic effect required for a drug. AI can help speed up this process with accurate predictions. It can also help make other aspects of drug development faster.
Efficiency: AI models can work tirelessly with no chances of human error. This makes them much more efficient and productive when compared to humans.
Applications of AI in drug discovery
From these numbers, it is evident that something needs to be done to improve the efficiency of the drug discovery process. This is why incorporating cutting-edge technology like Artificial Intelligence is important. According to a report from Bekryl, AI has the potential to offer over US$70 billion in savings for the drug discovery process by 2028. AI can help improve the drug discovery process in the following ways:
- AI for primary drug screening
Artificial intelligence technology can quickly and accurately recognize images containing distinct objects or features. Recognizing images by manual visual analysis is a very hectic job and becomes very inefficient during the analysis of big data. This is why using AI-based computing technologies can be very beneficial. For cell target classification or diagnosis, the AI model needs to be trained to rapidly and automatically identify the different features of cell types.
- Drug target identification and validation
The use of artificial intelligence technology in identifying targets helps researchers to properly analyze all the relevant evidence so as to gain a better understanding of the disease and its underlying biology. AI can synthesise data, and then come to conclusions regarding the best targets. This allows researchers to make better decisions about which targets are most likely to succeed.
- Processing biomedical, clinical and patient data
AI models help researchers deal with large volumes of biomedical and patient data. They can also provide intuitive intuitive insights about drug candidates and identify novel pathways, targets and biomarkers by reviewing this data.
- Planning chemical synthesis
After a particular molecule has been virtually screened for its potential bioactivity and toxicology profile, researchers begin to search for an optimal chemical synthesis pathway to synthesize the drug candidates. This process is very difficult and inefficient. ML models trained on empirical data can now be used in the following ways-
- To predict the probability of a transformation at a particular branching position.
- To guide the selection of the random steps.
- Drug optimization and repurposing
Drug repurposing is being used by many pharmaceutical companies as it provides a high value approach that presents less risk of unexpected toxicity or side effects in human trials and R&D spend. AI can be used to provide better insights on the polypharmacology of drugs to improve drug development success rates by identifying offset targets and unwanted toxic effects, while providing opportunities for drug repurposing.
- Predictive biomarkers
ML-based biomarker discovery and drug sensitivity predictive models have been proven to help boost clinical trial success rates. They can also improve the understanding of the drug action mechanism and to identify the appropriate drug for patients. Late-stage clinical trials take a lot of time and money to conduct, so it can be extremely advantageous to build, validate and apply predictive models earlier, using preclinical and early-stage clinical trial data. A translational biomarker can be predicted using ML approaches on preclinical data sets.
Challenges in AI adoption for drug discovery
- Copyright complications: In many countries, established patent laws state that any development facilitated solely by AI is under the public domain and can not be patented. This is why pharma companies who are using AI for drug discovery must go through an intense process to copyright their work and secure patent rights.
- Insufficient data: AI models rely heavily on data for proper training. Without an adequate amount of data, the AI model will not be able to function properly or respond to variations in problem situations.
- Security: AI-driven personalized medicine involves the use of an individual’s genetic code to develop drugs. This requires personal information to be supplied, which creates risks if precautionary measures are not taken.
- Less popular: AI is an emerging technology and is still being met with suspicion. Not all patients would be willing to try drugs that are developed by a machine, due to reservations about safety and reliability.
- Duplicate research: AI models are not aware of the drugs that have already been developed. In some cases, these models can waste time and energy in developing drugs that already exist. This can cause significant losses for the company.
By applying AI in drug discovery process, the efficiency of the drug discovery process can be increased to a very large extent. We can see applications of AI in areas like cell sorting, cell classification, quantum mechanics, calculation of compound properties, computer-aided organic synthesis, designing new molecules, predicting the 3D structures of target proteins, and more.
Many firms in healthcare industries have also begun to recognize the benefits of implementing AI in drug discovery and development process.
But, will it accelerate the COVID-19 drug development? Read More