5 May, 2023

AI for drug discovery has become a promising and rapidly growing field in the pharmaceutical industry. With the help of machine learning, deep learning, and natural language processing, scientists can process vast amounts of data to accelerate drug discovery and development.

Traditionally, drug discovery has been a time-consuming and costly process that involves extensive experimentation, testing, and analysis. However, with the advancements in AI technology, researchers can streamline the drug discovery process and potentially reduce the time and costs associated with developing new drugs.

One of the key benefits of AI in drug discovery is its ability to analyze large datasets quickly and accurately. By using machine learning algorithms, scientists can identify potential drug targets, predict drug efficacy, and identify potential side effects before even testing the drug in a laboratory setting.

AI can also help researchers with drug repurposing, which involves identifying new uses for existing drugs. By analyzing data from clinical trials and electronic health records, AI algorithms can identify patterns and relationships that suggest the potential of a drug for a different use than originally intended.

Another important application of AI in drug discovery is in virtual screening. This involves using computer simulations to identify compounds that have the potential to interact with specific proteins or disease targets. By simulating the interaction between molecules and proteins, AI can predict the effectiveness of a drug before it is even synthesized, saving time and resources in the drug development process.

Furthermore, AI can be used in the design of clinical trials, which are essential for testing the safety and effectiveness of new drugs. By analyzing patient data and outcomes, AI can help researchers identify potential biases, optimize trial design, and improve patient selection criteria.

Despite the potential benefits of AI for drug discovery, there are also challenges and limitations to consider. One of the challenges is the quality and availability of data. To train AI algorithms effectively, researchers need access to large, high-quality datasets. However, data in the healthcare industry can be fragmented and challenging to access due to privacy concerns and regulatory restrictions.

Another challenge is the complexity of biological systems. While AI can help identify potential drug targets and predict drug efficacy, the biological mechanisms of many diseases and the way drugs interact with the body are not fully understood. Therefore, researchers need to interpret AI predictions in the context of existing scientific knowledge to ensure that their findings are valid and meaningful.

Cost of drug discovery is also rising. Cost A 2020 pricing analysis estimates that the cost to develop a single drug application ranges from $314 million to $2.8 billion, with an estimated median capitalized research and drug development cost per therapeutic product at $985 million. These cost-intensive investments cause upward pressure on end-user prices. An added dilemma is that the high cost of developing new products is occurring at a time when the patents on some of pharma’s most reliable money-makers expire, which causes increased pressure on profit margins.

Finally, a significant challenge of AI is when it is used for cybercrime. Hackers use AI-enhanced algorithms to search work-in-progress and archived documents for vulnerable points of attack, then make copies of confidential documents and/or insert malware code. Cyber-risk monitoring company Black Knight reports that some 10 percent of pharmaceutical manufacturers are highly susceptible to a ransomware attack, and that more than 12 percent of pharma industry vendors are likely to incur a ransomware attack.

To respond to the hacker challenge, drug discovery companies need to update their security protocols to protect confidential archived documents as well as collaborative work-in-progress documents. Fortunately, a quality security vendor like ShareVault can provide that level of security.

The ShareVault online platform provides an ultra-secure environment for archiving and sharing critical drug discovery documents called a Virtual Deal Room (VDR). An authorized user can access ShareVault using two-factor authentication, from any location or authorized device, at any time of the day. Every document is automatically encrypted. The VDR administrator can set time and content limits on access and can deauthorize access at any time. Once drug discovery team members are in the secure VDR environment, they can access databases and document archives and generate and share new documents exactly as they would normally do, using Word, Excel, and other software. Access can be extended to regulators, partners, investors, and other third parties, under the same security protocols as drug discovery team members.

In conclusion, AI has the potential to revolutionize drug discovery and accelerate the development of new drugs. By leveraging machine learning and other AI technologies, researchers can streamline the drug discovery process, identify potential drug targets, and design more effective clinical trials. While there are challenges to consider, the future of AI for drug discovery looks promising and could lead to significant advancements in the field of medicine.