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Dissecting errors in machine learning for retrosynthesis: a granular metric framework and a transformer-based model for more informative predictions

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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

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Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks

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Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis

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Improving route development using convergent retrosynthesis planning

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Developing BioNavi for Hybrid Retrosynthesis Planning

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Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

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RLSynC: Offline–Online Reinforcement Learning for Synthon Completion

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Bidirectional Chemical Intelligent Net: A unified deep learning–based framework for predicting chemical reaction

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Chinese Chemical Letters, 2025, 36, 110968
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ASKCOS: Open-Source, Data-Driven Synthesis Planning

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G2Retro as a two-step graph generative models for retrosynthesis prediction

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DeepCubist: Molecular Generator for Designing Peptidomimetics based on Complex three-dimensional scaffolds

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RPSubAlign: a novel sequence-based molecular representation method for retrosynthesis prediction with improved validity and robustness

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$$\texttt {DiffER}$$: categorical diffusion ensembles for single-step chemical retrosynthesis

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Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment

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RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking

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RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts

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Accelerating the inference of string generation-based chemical reaction models for industrial applications

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Advances in machine learning with chemical language models in molecular property and reaction outcome predictions

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Advances in deep learning-based 3D molecular generative models

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RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints

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Site-specific template generative approach for retrosynthetic planning

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The converging moment of chemistry and artificial intelligence: a review and outlook

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Challenging Complexity with Simplicity: Rethinking the Role of Single-Step Models in Computer-Aided Synthesis Planning

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RetroCaptioner: beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation

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Developing ChemDFM as a large language foundation model for chemistry

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Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions

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RadicalRetro: A Deep Learning-Based Retrosynthesis Model for Radical Reactions

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Graph neural networks for conditional de novo drug design

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GraphGPT: A Graph Enhanced Generative Pretrained Transformer for Conditioned Molecular Generation

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HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model

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Diverse and feasible retrosynthesis using GFlowNets

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Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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Retrosynthesis prediction with an iterative string editing model

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Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis

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Machine learning-assisted retrosynthesis planning: Current status and future prospects

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DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis

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From reactants to products: computational methods for biosynthetic pathway design

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Application of Transformers to Chemical Synthesis

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Single-step retrosynthesis prediction via multitask graph representation learning

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Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing

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Machine learning-guided strategies for reaction conditions design and optimization

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Tensor databases empower AI for science: A case study on retrosynthetic analysis

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Bridging chemistry and artificial intelligence by a reaction description language

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SynTemp: Efficient Extraction of Graph-Based Reaction Rules from Large-Scale Reaction Databases

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Recent advances in deep learning for retrosynthesis

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Artificial Intelligence in Retrosynthesis Prediction and its Applications in Medicinal Chemistry

Lanxin Long, Rui Li and Jian Zhang

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