AI in Drug Discovery: A Computerized drug development


AI in Drug Discovery: A Computerized drug development


Artificial intelligence (AI), and the subfield of machine learning (ML), study the processes and practicalities of enabling machines to skillfully perform intelligent tasks, without explicitly being programmed for those tasks. Recently, AI systems have neared or surpassed human performance in several tasks, such as game playing and image recognition, but these have typically been quite narrow and focused domains. Nonetheless, AI in its various forms is today successfully applied across a large range of domains and for challenging tasks, ranging from robotics, speech translation, image analysis and logistics to its ongoing use in designing molecules.

AI plays a role in drug discovery. There is a wealth of existing or collectable data to better understand diseases. This information also makes it possible to develop drugs which can increasingly be personalized to the patient’s profile. The information collected makes it possible to create 3D models, “design” and test a molecule virtually. In addition, some of these technologies allow the analysis of a patient’s profile to determine the patients who will best respond to the new drugs.

Involvement of AI in the development of a pharmaceutical product from the bench to the bedside can be imagined given that it can aid rational drug design, assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development. E-VAI is an analytical and decision-making AI platform developed by Eularis, which uses ML algorithms along with an easy-to-use user interface to create analytical roadmaps based on competitors, key stakeholders, and currently held market share to predict key drivers in sales of pharmaceuticals, thus helping marketing executives to allocate resources for maximum market share gain, reversing poor sales and enabled them to anticipate where to make investments. The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. It can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design.

AI has stimulating opportunities to flourish in the biopharmaceutical arena.

The current AI initiatives by the top biopharmaceutical companies include;

(a) Mobile platform to improve health outcomes-the ability to recommend patients by means of real-time data collection and thus improve patient outcomes.

(b) Personalized medicine-the ability to evaluate big database of patient so as to recognize cure options using a cloud-based system.

(c) Acquisitions galore-New start-up companies are combining the DrtificiDl intelligence and healthcare to nourish the innovation requirements of large biotech firms.

(d) Drug discovery-Pharma companies in conjunction with soіwDre companies are trying to implement the most cutting-edge technologies in the costly and extensive process of drug discovery.

AI can provide radical ideas for medication and therapies through data retrieved from genomics, proteomics and other life science disciplines that could bring advancement in the drug discovery and development process. While many approaches have come into the process, it would be an integral part for AI in the drug discovery process. Even though these applications are new, they are promising in terms of the precision, accuracy and perfection.


AI based advanced applications:

  • AI based nano-robots for drug delivery: These comprise mainly integrated circuits, sensors, power supply, and secure backup of data, which are maintained via computational technologies.
  • AI in terms of drug delivery and synergism or antagonism prediction: Several combinations of drugs are approved and marketed to treat complex diseases, such as TB and cancer, because they can provide a synergistic effect for quick recovery.
  • AI emergence in nano-medicine: Nanomedicines use nanotechnology and medicines for the diagnosis, treatment, and monitoring of complex diseases, such as HIV, cancer, malaria, asthma, and various inflammatory diseases

The AI’s advancement, along with its remarkable tools, continuously aims to reduce challenges faced by pharmaceutical companies, impacting the drug development process along with the overall lifecycle of the product, which could explain the increase in the number of start-ups in this sector. The current healthcare sector is facing several complex challenges, such as the increased cost of drugs and therapies, and society needs specific significant changes in this area. With the inclusion of AI in the manufacturing of pharmaceutical products, personalized medications with the desired dose, release parameters, and other required aspects can be manufactured according to individual patient need. Using the latest AI-based technologies will not only speed up the time needed for the products to come to the market, but will also improve the quality of products and the overall safety of the production process, and provide better utilization of available resources along with being cost-effective, thereby increasing the importance of automation.