Exploring chemical reaction space with machine learning models: Representation and feature perspective
Chemical reactions serve as foundational building blocks for organic chemistry and drug
design. In the era of large AI models, data-driven approaches have emerged to innovate the …
design. In the era of large AI models, data-driven approaches have emerged to innovate the …
Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design
Combining discrete and continuous data is an important capability for generative models.
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …
Dirichlet flow matching with applications to dna sequence design
Discrete diffusion or flow models could enable faster and more controllable sequence
generation than autoregressive models. We show that na\" ive linear flow matching on the …
generation than autoregressive models. We show that na\" ive linear flow matching on the …
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
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 …
discovery. Current data-driven strategies employ one-step retrosynthesis models and search …
Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment
Motivation Retrosynthesis planning poses a formidable challenge in the organic chemical
industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step …
industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step …
Alignment is Key for Applying Diffusion Models to Retrosynthesis
Retrosynthesis, the task of identifying precursors for a given molecule, can be naturally
framed as a conditional graph generation task. Diffusion models are a particularly promising …
framed as a conditional graph generation task. Diffusion models are a particularly promising …
Cometh: A continuous-time discrete-state graph diffusion model
Discrete-state denoising diffusion models led to state-of-the-art performance in graph
generation, especially in the molecular domain. Recently, they have been transposed to …
generation, especially in the molecular domain. Recently, they have been transposed to …