Drug discovery is a challenging vertical in the healthcare industry as the researchers face slim odds while trying to convert a molecule into a medicine. Most studies put the success rate for drug discovery as below average as the chances for success stands at one in ten. However, many researchers are of the opinion that artificial intelligence (AI) can be the game changer for drug discovery with the help of advanced computational tools such as machine learning (ML) and molecular dynamics simulation.
The traditional way of drug discovery project starts with basic research that is aimed to uncover the targets which are susceptible to attacks like a disease-related protein receptor on the surface of particular cells. After uncovering the targets, scientists use techniques like high-throughput screening to observe which compound bind the target. The researchers use various other measures to fine tune the structure of the medicine. AI tools can help scientists to look for patterns in large data sets that can be missed by the human eye. The patterns can enable scientists in predicting the compounds that have a high probability of turning into medicines.
AI technology can help researchers to connect the dots in their research for drug discovery. The AI-enabled solutions can be applied to the whole genome, phenotypic, and clinical data. The ML technique can help researchers to combine multiple parameters collected from simulation to generate a model that differentiates biochemically active from biochemically inactive molecules.
Although the use of AI in drug discovery has started gaining steam, many healthcare companies still use the traditional methods of drug discovery. The AI solutions need data for effective results and as AI tools get more healthcare data; they will be able to add significant advantages to the drug discovery. The AI technology can help researchers to finish mundane and repetitive tasks in real-time, allowing researchers to concentrate on more productive works.