The systematic, autonomous optimization of discrete reaction parameters in organic synthesis remains unexplored, offering the potential to revolutionize synthetic methodology and expand the scope and efficiency of reaction development.
Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making.
The integration of artificial intelligence (AI) and machine learning (ML) in drug design has the potential to transform small molecule drug discovery in the pharmaceutical industry, enhancing the efficiency and productivity of the drug design and discovery process.