Artificial Intelligence has transformed biomedical research in terms of Drug Discovery and Design. It has made the process more efficient and has paved a path for the discovery and development of inexpensive and efficient drugs and rational structure-based drug design, with reduced time and costs. Pharmaceutical companies are using Machine Learning Models and Advanced AI Insights to analyze and use complex datasets as a solution to the increased cost of new drug development.
Drug discovery using AI
On average, a new drug coming to the market has to go through phases that involve testing the drug on sample groups for years, and with costs involved up to billions. It also involves the standard approaches of testing how different molecules of the drug composition interact with different targets. The preclinical development phase of drug discovery tests potential drug targets on animal samples.
During this phase, trials can be accelerated with minimal errors using AI models, enabling researchers to easily find the permutations and combinations of the drug interaction with the animal test sample model. AI can also facilitate participant monitoring during trials, generating a larger set of data more quickly, and aid in participant retention by personalizing the trial experience. With advancements in AI, the automatic feature extraction ability of deep learning supports models with better accuracy and delivers more reliable results. Secondly, deep learning models’ generative ability can be utilized to speed up the drug discovery process with better prediction capability and a low failure rate.
Drug design using AI
Drug design is the process of finding new medicinal drugs based on the knowledge of biological targets. How strongly a molecule can bind to a target is the main goal of a drug design phase. With the computational machine learning models, the computational affinity of a compound for synthesis can be predicted, and hence only one compound needs to be synthesized saving enormous time and cost.
Artificial intelligence (AI) proves very efficient in various fields of drug design that include virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties.
Many pharmaceutical companies are riding the wave of Drug Design and Discovery using AI. For example, AstraZeneca using AI is researching new drugs on chronic kidney diseases. Bristol-Myers Squibb (BMS) has deployed machine learning models in finding data patterns that are associated with CYP450 inhibitors, these patterns help to reduce adverse side effects and interactions of the drug-in-development.
GSK Pharma has its in-house AI unit which has developed a machine-learning algorithm to identify protein crystals. Not only the big pharma players but the mid-sized pharmaceutical companies and startups are also taking advantage of these advancements of AI in drug discovery and design.
The next decade will see a transition from traditional drug design and discovery methodologies to highly intelligent AI-based algorithms that will deal with the new scale and complexity of the data used in drug design and discovery.