Retrosynthesis planning is critical in decomposing complex target molecules into simpler, purchasable reactants. Traditional methods often struggle because they fail to account for the weakest link in the synthetic route, potentially leading to invalid synthesis trees. By reframing retrosynthesis as a worst-path optimization problem, the new method, InterRetro, provides a unique solution that ensures reliability in the synthesis process.
InterRetro interacts with tree-structured Markov Decision Processes (MDPs) to learn and reinforce values associated with the least favorable outcomes. This enhances the planning process, allowing for faster and more accurate synthesis, achieving impressive results on benchmark tests. The method proves especially effective with just a fraction of the training data, showcasing its potential implications for future research and industry applications in chemical synthesis and pharmaceutical development.
👉 Pročitaj original: arXiv AI Papers
