Reasoning Through Molecular Synthetic Pathways with Generative AI
“Large language models (LLMs) have become integral to daily life, powering applications from virtual assistants to complex problem-solving. Modern LLMs solve complex problems by generating a chain of thought (CoT), which is a series of intermediate reasoning steps that lead to a final answer. Combining CoT and test-time search methods, such as generating multiple CoT paths, are critical to the improved accuracy of recent LLMs.
Chemistry faces a similar challenge in molecular synthesis pathway prediction, where a pathway contains a series of intermediate synthesis steps. Pathway prediction is a critical step in drug, chemical, and materials development because a molecule, however promising, is only valuable if it can be synthesized. ReaSyn is a novel generative framework that efficiently predicts molecular synthesis pathways. It uses a unique chain of reaction (CoR) notation, inspired by the CoT approach in LLMs, combined with a test-time search algorithm…”
Source: developer.nvidia.com/blog/reasoning-through-molecular-synthetic-pathways-with-generative-ai