# SynKB: Semantic Search for Synthetic Procedures

arXiv: https://arxiv.org/abs/2208.07400 · HTML: https://mlsanity.com/paper/2208.07400/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Pre-train or Annotate? Domain Adaptation with a Constrained Budget](https://mlsanity.com/paper/2109.04711/) (2021)
- [An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols](https://mlsanity.com/paper/1805.00195/) (2018)
- [Process-Level Representation of Scientific Protocols with Interactive Annotation](https://mlsanity.com/paper/2101.10244/) (2021)
- [Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature](https://mlsanity.com/paper/2002.07339/) (2020)
- [Extracting a Knowledge Base of COVID-19 Events from Social Media](https://mlsanity.com/paper/2006.02567/) (2020)
- [Neural Extractive Search](https://mlsanity.com/paper/2106.04612/) (2021)

## Built on by (most cited first, 4 of 4)

- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology](https://mlsanity.com/paper/2310.10632/) (2023)
- [MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures](https://mlsanity.com/paper/2302.05597/) (2023)
- [Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding](https://mlsanity.com/paper/2210.06444/) (2022)

## Abstract (arXiv)

In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.