Drug Retrosynthesis

Drug retrosynthesis is a strategy used in organic chemistry and pharmaceutical science to plan the synthesis of a complex drug molecule by breaking it down into simpler starting materials. Planning synthesis routes for target molecules, like potential drugs, is complex. Traditional computing methods and existing machine learning solutions are limited by human knowledge, training time, and scalability issues.

Quantum Machine Learning (QML) is being explored, particularly Quantum Long Short-Term Memory (QLSTM). This quantum-classical hybrid approach uses Variational Quantum Circuits to process chemical data for reaction predictions.

This technology shows promise, demonstrating higher accuracy in predicting chemical reactions (80% versus 70% for classical methods) in initial testing. It offers the potential for exponentially greater information representation, significantly accelerating the development of new medicines and materials.