mlsanityData to 14 Oct 2026

BioNavi-NP: Biosynthesis Navigator for Natural Products

arXiv 2105.13121 ↗First posted 26 May 2021q-bio.QM cs.CE cs.LGcited by 1

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Abstract (arXiv)

Nature, a synthetic master, creates more than 300,000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs. To date, there are fewer than 30,000 validated NPs compounds involved in about 33,000 known enzyme catalytic reactions, and even fewer biosynthetic pathways are known with complete cascade-connected enzyme catalysis. Therefore, it is valuable to make computer-aided bio-retrosynthesis predictions. Here, we develop BioNavi-NP, a navigable and user-friendly toolkit, which is capable of predicting the biosynthetic pathways for NPs and NP-like compounds through a novel (AND-OR Tree)-based planning algorithm, an enhanced molecular Transformer neural network, and a training set that combines general organic transformations and biosynthetic steps. Extensive evaluations reveal that BioNavi-NP generalizes well to identifying the reported biosynthetic pathways for 90% of test compounds and recovering the verified building blocks for 73%, significantly outperforming conventional rule-based approaches. Moreover, BioNavi-NP also shows an outstanding capacity of biologically plausible pathways enumeration. In this sense, BioNavi-NP is a leading-edge toolkit to redesign complex biosynthetic pathways of natural products with applications to total or semi-synthesis and pathway elucidation or reconstruction.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 44 references that are in the map. First posted on arXiv 26 May 2021.

Builds on

5 of 44 references.

References · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Predictioncited by 229
  3. 2017Learning to Plan Chemical Synthesescited by 179
  4. 2020Retro*: Learning Retrosynthetic Planning with Neural Guided A* Searchcited by 76
  5. 2019Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Treescited by 10

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Citing papers per year: 2021: 1.

1 paper.