Remodelling structure-based drug design using machine learning

摘要: To keep up with the pace of rapid discoveries in biomedicine, a plethora research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Design (SBDD). In past few decades, SBDD played stupendous role identification novel drug-like molecules are capable altering structures and/or functions target macromolecules involved different disease pathways and networks. Unfortunately, post-delivery drug failures due adverse interactions have constrained use biomedical applications. However, recent technological advancements, along parallel surge clinical led concomitant establishment other powerful computational techniques such as Artificial Intelligence (AI) Machine Learning (ML). These leading-edge tools ability successfully predict side-effects wide range drugs eventually taken over field design. ML, subset AI, is robust tool data analysis analytical model building minimal human intervention. It based on algorithms huge sets 'training data' inputs new output values, which improve iteratively through experience. this review, brief discussion evolution discovery process, we focused methodologies pertaining advancements machine learning. This specific examples, also emphasises tremendous contributions ML while exploring possibilities for future developments.