Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically
breaking down molecules into readily available building block compounds. Having a long …

Retrosynthesis is the task of proposing a series of chemical reactions to create a desired
molecule from simpler, buyable molecules. While previous works have proposed algorithms …

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

Plants, as a sessile organism, produce various secondary metabolites to interact with the
environment. These chemicals have fascinated the plant science community because of …

Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph
patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are …

Motivation Retrosynthesis is a critical task in drug discovery, aimed at finding a viable
pathway for synthesizing a given target molecule. Many existing approaches frame this task …

Graph Neural Networks (GNNs) have been widely adopted for drug discovery with
molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range …

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

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 …

The advancement of machine learning and the availability of large-scale reaction datasets
have accelerated the development of data-driven models for computer-aided synthesis …