ConfRetro: a 3D-aware template-free method for enhancing retrosynthesis via molecular conformer information

Abstract:Motivation: Retrosynthesis plays a crucial role in organic synthesis and drug discovery, focusing on identifying a set of reactants capable of synthesizing a target product molecule. Although the existing approaches have shown promising results, they do not fully exploit 3D conformer information and molecular spatial structure, which can hinder stereochemically consistent and chemically plausible predictions. Results: To tackle this problem, we propose ConfRetro, a Transformer-based template-free method that integrates molecular conformer information and spatial structure. We devise an Atom-align Fusion module to combine 3D positional information at the model input stage, ensuring alignment between atom tokens and corresponding 3D representations. Furthermore, we design a Distance-weighted Attention mechanism to guide self-attention, constraining the receptive field of model and emphasizing chemically relevant atom pairs in 3D space. Experiments conducted on the USPTO-50K and USPTO-FULL datasets demonstrate that ConfRetro significantly outperforms existing template-free approaches, achieving a new state-of-the-art performance. Case studies further highlight its capability to predict accurate and chemically plausible reactants, even for target molecules with intricate structures. Moreover, when plugged into a standard retrosynthetic search, ConfRetro recovers feasible synthetic routes for multiple representative drug molecules (e.g. Camptothecin). Availability and implementation: ConfRetro is available at this https URL. Archival snapshot is available at this https URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2501.12434 [cs.LG]

(or arXiv:2501.12434v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2501.12434 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1093/bioinformatics/btag575 Focus to learn more DOI(s) linking to related resources

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