Accelerating Large-Scale Cheminformatics Using a Byte-Offset Indexing Architecture for Terabyte-Scale Data Integration

TL;DR

This paper demonstrates a byte-offset indexing approach that significantly accelerates large-scale chemical database integration, enabling efficient, validated dataset creation for cheminformatics and machine learning.

Contribution

It introduces a scalable byte-offset indexing architecture that overcomes brute-force limitations, ensuring data integrity and efficiency at hundreds of millions of entries.

Findings

Achieved 740-fold performance improvement over brute-force methods

Validated 176 million chemical entries with collision-free identifiers

Provided benchmarks and trade-offs for large-scale data integration

Abstract

The integration of large-scale chemical databases represents a critical bottleneck in modern cheminformatics research, particularly for machine learning applications requiring high-quality, multi-source validated datasets. This paper presents a case study of integrating three major public chemical repositories: PubChem (176 million compounds), ChEMBL, and eMolecules, to construct a curated dataset for molecular property prediction. We investigate whether byte-offset indexing can practically overcome brute-force scalability limits while preserving data integrity at hundred-million scale. Our results document the progression from an intractable brute-force search algorithm with projected 100-day runtime to a byte-offset indexing architecture achieving 3.2-hour completion - a 740-fold performance improvement through algorithmic complexity reduction from $O(N \times M)$ to $O(N + M)$. Systematic validation of 176 million database entries revealed hash collisions in InChIKey molecular identifiers, necessitating pipeline reconstruction using collision-free full InChI strings. We present performance benchmarks, quantify trade-offs between storage overhead and scientific rigor, and compare our approach with alternative large-scale integration strategies. The resulting system successfully extracted 435,413 validated compounds and demonstrates generalizable principles for large-scale scientific data integration where uniqueness constraints exceed hash-based identifier capabilities.