Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nathaniel H. Stanley
dblp:410/8558
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-2074-0722ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
2 papers |
Knowledge graphs · 50% Information retrieval · 50% | |
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 7 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › document understanding
document layout analysis |
0.9 | 1 | 2025 | MoleculeMiner: Extracting and Linking Molecule Figures with Tabular Metadata · IJCAI 2025 |
Bioinformatics and computational biology
drug discovery |
0.9 | 1 | 2025 | Targeted Multi-Modal Passage Search for Molecules and their Synthesis Pathways · SIGIR 2025 |
Bioinformatics and computational biology › systems bioinformatics
metabolic route search |
0.9 | 1 | 2025 | Targeted Multi-Modal Passage Search for Molecules and their Synthesis Pathways · SIGIR 2025 |
Knowledge graphs
entity linking |
0.9 | 1 | 2025 | MoleculeMiner: Extracting and Linking Molecule Figures with Tabular Metadata · IJCAI 2025 |
Knowledge graphs
knowledge graph construction |
0.9 | 1 | 2025 | MoleculeMiner: Extracting and Linking Molecule Figures with Tabular Metadata · IJCAI 2025 |
Information retrieval
multimodal retrieval |
0.9 | 1 | 2025 | Targeted Multi-Modal Passage Search for Molecules and their Synthesis Pathways · SIGIR 2025 |
Information retrieval › document retrieval
passage retrieval |
0.9 | 1 | 2025 | Targeted Multi-Modal Passage Search for Molecules and their Synthesis Pathways · SIGIR 2025 |
Methods — techniques the papers use, named apart from their topics
substructure matching · 1.7structural similarity · 1.7pixel-based self-attention positional embedding · 1.7molecular image parsing · 1.7SMILES matching · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | MoleculeMiner: Extracting and Linking Molecule Figures with Tabular MetadataabstractDespite an ongoing shift in automated chemical literature search methods, many are fairly limited in ability to find very specific relevant information about a drawn molecule and its associated property data. We aim to tackle the challenge of converting drawn molecules to a machine readable representation and co-reference any associated molecule data. MoleculeMiner is a system where a user can feed in their own patent or paper to obtain each drawn molecule along with any specific metadata (chemical name, chemical reactivity, yield, purity etc.) provided anywhere in the PDF in a tabular format, using an interactive user-friendly environment. We also present MolScribeV2, a molecular image parser which improved upon the original MolScribe by introducing pixel-based self attention positional embedding technique. Along with other changes, MolScribeV2 is robust to varied styles of compound drawings commonly found in patents and papers--scanned or born digital. Our extraction and user interactive system can be found at https://github.com/insitro/MoleculeMiner. Abhisek Dey, Nathaniel H. Stanley |
IJCAI | 2 |
| 2025 | Targeted Multi-Modal Passage Search for Molecules and their Synthesis PathwaysabstractWe present a chemical extraction and search pipeline intended to support information tasks related to drug discovery. Commonly used search tools for drug discovery such as Reaxys and SciFinder do not allow users to obtain retrieval results at the passage level. To address this, we present a passage retrieval tool for chemical patents that supports queries combining text and molecule diagrams expressed in SMILES. When SMILES is provided as a part of a query, the system refines text retrieval results through matching both textual names and drawn figures based on extracted SMILES representations. Molecule matches are obtained through substructure matching and structural similarity. This functionality was motivated by a chemist's need to find synthesis pathways for specific molecules containing a substructure of interest that binds and thus inhibits specific human genes. For this demonstration, we index a collection of 131 PDF patents categorized into 12 specific genes enabling a user to search on them. There are 32,301 document pages in the collection. Our user interface can be accessed at https://unichemfinder.gccis.rit.edu/. Our source code and data is available at https://gitlab.com/dprl/unichemfinder. Abhisek Dey, Nathaniel H. Stanley, Richard Zanibbi |
SIGIR | 2 |