Artificial Intelligence has accelerated the world of the healthcare industry. It is influential in improving diagnostics tools, interacting better with patients recovering from surgery or with mental illness, transporting medical samples and medicines. And now it is proving its mettle in drug discovery too.

From target discovery to adaptive clinical trial design, AI has come a long way. Besides cutting time, it has also been key to identifying numerous compounds that has the potential of treating or preventing diseases. A traditional approach would have taken lots of expenses and development time without any guarantee of success. Getting a single drug to market consumed a shocking period of 10 to 12 years, with an estimated price tag of nearly US$2.9 billion. It's no surprise that scientists in pharmaceuticals and biotech companies are looking for alternative ways to increase efficiency.

AI is an imperative asset as it is an annotator for clinical data. Almost two-thirds of the healthcare data is ambiguously structured. More data more is the demand for computation approaches and empirical shortcuts. With the help of natural language processing, AI can quickly explore and sift through arrays of this unstructured data to read, understand and categorize them. It can utilize algorithms, heuristics, and pattern matching to figure out physico-chemical insights that can qualify for the discovery of new compounds for medical purposes. Or it can use historical pieces of evidence to predict the possibility of a compound that shares similarities with the hypothetical ones scientists are looking for. Because of this ability, AI enables drug discovery teams to be far more focused and efficient.

When these promising benefits of AI are coupled with avant-garde automation technology, humans will achieve limitless prospective applications. The automation market brings diverse options of tools for the bio-pharmacy community. Considering the immense expenditure on R&D, it enables the lifting of blockages that occur in many processes downstream to target identification and screening. In other words, it reduces late-stage compound drug rejections. Further, it helps in carrying out repetitive and menial tasks like picking and placing sample vials, labeling, etc… Thus saving skilled workforce hours and gives better financial returns. Along with AI systems, introducing the automation process can improve a design hypothesis for a drug through feedback analysis. Now it is possible to have a fully automated multi-step and parallel synthesis of highly complex molecules at ratios from nanograms to grams.

This can potentially fast track time frames for compound discovery and optimization and enable more functional and iterative searches of chemical space.

If one thinks of AI most benignly, it helps us to streamline the exploding data from various outputs. By making the data comprehensive, it enables researchers to come up with better solutions. However, not everyone is welcoming this transition as they fear redundancies in the healthcare sector and isolation among lab workers. While it minimizes human error, a single minor glitch can cause horrible repercussions. Therefore one must be careful in defining and refining the program codes and use them as the requirement. Also since now man is the master of the machines and can predict their outcome, he must take this leap of faith over risks. As automation led AI is spearheading the medical world, pharmaceutical firms are showing increased hunger for data. Paired with customized automation can allow compatibility, configurability, and flexibility with other resources. Thus bringing good ROI for drug research companies and step up the productivity to meet the rising market demands.