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 …
Re-evaluating Retrosynthesis algorithms with syntheseus
Automated Synthesis Planning has recently re-emerged as a research area at the
intersection of chemistry and machine learning. Despite the appearance of steady progress …
intersection of chemistry and machine learning. Despite the appearance of steady progress …
Retrogfn: Diverse and feasible retrosynthesis using gflownets
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a
target molecule, which is a crucial task in molecular discovery. Although a target molecule …
target molecule, which is a crucial task in molecular discovery. Although a target molecule …
Molecule-edit templates for efficient and accurate retrosynthesis prediction
Retrosynthesis involves determining a sequence of reactions to synthesize complex
molecules from simpler precursors. As this poses a challenge in organic chemistry, machine …
molecules from simpler precursors. As this poses a challenge in organic chemistry, machine …
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 …
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Drug-Target binding Affinity (DTA) prediction is essential for drug discovery. Despite the
application of deep learning methods to DTA prediction, the achieved accuracy remain …
application of deep learning methods to DTA prediction, the achieved accuracy remain …
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid
chemists in finding appropriate reactant molecules and synthetic pathways given …
chemists in finding appropriate reactant molecules and synthetic pathways given …
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 …
Aligned Diffusion Models for Retrosynthesis
Retrosynthesis, the task of identifying precursors for a given molecule, can be naturally
framed as a conditional graph generation task, with diffusion models being a particularly …
framed as a conditional graph generation task, with diffusion models being a particularly …