Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial
intelligence (AI) is increasingly employed to expedite the process. However, existing …

Abstract Machine learning, particularly graph learning, is gaining increasing recognition for
its transformative impact across various fields. One such promising application is in the …

Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry
that have recently garnered attention from both the machine learning and drug discovery …

Abstract Machine learning-assisted retrosynthesis planning aims to utilize machine learning
(ML) algorithms to find synthetic pathways for target compounds. In recent years, with the …

Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning
(DL) techniques in computer-aided synthesis design. While template-free DL models are …

Retrosynthesis is the process of determining the set of reactant molecules that can react to
form a desired product. Semitemplate-based retrosynthesis methods, which imitate the …

Motivation Retrosynthesis identifies available precursor molecules for various and novel
compounds. With the advancements and practicality of language models, Transformer …

Motivation Molecular representation learning (MRL) models molecules with low-dimensional
vectors to support biological and chemical applications. Current methods primarily rely on …

Molecule synthesis through machine learning is one of the fundamental problems in drug
discovery. Current data-driven strategies employ one-step retrosynthesis models and search …

In the process of drug retrosynthesis, identifying the reaction centers where the chemical
reactions occurring is an important fundamental issue in semi-templated models. However …