Retrosynthetic route planning is essential for designing efficient pathways to synthesize complex molecules, serving as a cornerstone in drug discovery and organic synthesis. Sequence-based models have become a predominant approach in retrosynthetic route planning, yet its validity and robustness remain limited by the challenges in molecular representation methods. Current methods typically treat reactants and products as independent molecules, overlooking structural relationships crucial for accurate synthesis predictions. Herein, we introduce RPSubAlign, a molecular sequence representation method specifically tailored for retrosynthetic tasks, which aligns common substructures between reactants and products to enhance the validity and robustness of sequence-based models. Compared with conventional random and root-alignment representations, RPSubAlign achieves better performance on the USPTO-50K and USPTO-MIT datasets, improving up to a 34.8% increase in Top-N accuracy (with Self-Referencing Embedded Strings representation) and demonstrating enhanced stability across various data augmentation scenarios. RPSubAlign significantly improves syntactic validity, reaching 86.64% on USPTO-50K and 96.45% on USPTO-MIT (with Simplified Molecular Input Line Entry System representation), outperforming baseline methods. These results highlight RPSubAlign as a robust, effective approach for molecular characterization method for retrosynthesis predictions. Code for RPSubAlign is available at https://github.com/Aminoacid1226/RPSubAlign.

SUMMARY: Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the 'Rule of Three'. AVAILABILITY AND IMPLEMENTATION: https://github.com/yydiao1025/MacFrag. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Although in silico drug discovery approaches are crucial for the development of pharmaceuticals, their potential advantages in agrochemical industry have not been realized. The challenge for computer-aided methods in agrochemical arena is a lack of sufficient information for both pesticides and their targets. Therefore, it is important to establish such knowledge repertoire that contains comprehensive pesticides' profiles, which include physicochemical properties, environmental fates, toxicities and mode of actions. Here, we present an integrated platform called Pesticide-Target interaction database (PTID), which comprises a total of 1347 pesticides with rich annotation of ecotoxicological and toxicological data as well as 13 738 interactions of pesticide-target and 4245 protein terms via text mining. Additionally, through the integration of ChemMapper, an in-house computational approach to polypharmacology, PTID can be used as a computational platform to identify pesticides targets and design novel agrochemical products. AVAILABILITY: http://lilab.ecust.edu.cn/ptid/. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.