Drug development is a fairly complex and challenging task. Creating effective medicines without adverse side effects is almost nearly impossible. Before designing a drug, medical chemists need to consider a range of drug interactions. This is because of wide differences in drug interactions from one patient to another. Moreover, selecting and designing most promising drug candidates is indeed hectic due to some laboratory limitations.
For selecting a promising drug, designers need to choose from approximately 1060 theoretically synthesized drug-like molecules. To understand drug synthesis theoretically, the designers need to gain expertise in medicinal chemistry. Thus, to cater such big challenge, researchers are thinking to deploy artificial intelligence (AI) in pharmaceutical research.
According to Gisbert Schneider from Eth Zurich, implementing AI in drug designing would give a thrust to pharmaceutical research. This is because of the AI potentiality in assisting chemists during drug design process more efficiently than humans. Moreover, incorporating AI in drug design process also holds promise to make better decisions in drugs selecting procedure.
AI Could Suggest New Chemical Structures of Drugs for Better Actions
AI sounds like a perfect partner in the pharmaceutical lab as it promises to discover and deliver drugs faster. However, before incorporating AI into drug discovery, scientists need to concede their imperfect understanding about human disease mechanisms. Only after gaining proper knowledge of human diseases, scientists can present the appropriate data to machine intelligence. Such proper data would help AI in learning meaningful relationships between drug candidates and their respective physiological effects.
Integrating AI in automated drug design process would require a complete new thinking. It would revamp the entire settings which include the technology and software of recent years. AI would bring a high degree of accuracy in drug designing process. Scientists from Eth Zurich are also hoping for AI to predict the drugs’ effects at an earlier stage of development.